{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of the GBM GridSearchCV results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file illustrates how to evaluate the [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) results for sklearn's [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) obtained after first running `sklearn_gbm_tuning.py` in this directory to test various hyperparameter combinations and store the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-27T16:38:05.452631Z",
     "start_time": "2018-12-27T16:38:05.265720Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "import os\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import graphviz\n",
    "\n",
    "from statsmodels.api import OLS, add_constant\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-27T16:38:05.568407Z",
     "start_time": "2018-12-27T16:38:05.557892Z"
    }
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "sns.set_style(\"whitegrid\")\n",
    "np.random.seed(42)\n",
    "pd.options.display.float_format = '{:,.4f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-27T16:38:05.996608Z",
     "start_time": "2018-12-27T16:38:05.848624Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('model_tuning.h5') as store:\n",
    "    test_feature_data = store['holdout/features']\n",
    "    test_features = test_feature_data.columns\n",
    "    test_target = store['holdout/target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GBM GridsearchCV with sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need OneStepTimeSeriesSplit because stored GridSearchCV result expects it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-27T16:38:09.973558Z",
     "start_time": "2018-12-27T16:38:09.967002Z"
    }
   },
   "outputs": [],
   "source": [
    "class OneStepTimeSeriesSplit:\n",
    "    \"\"\"Generates tuples of train_idx, test_idx pairs\n",
    "    Assumes the index contains a level labeled 'date'\"\"\"\n",
    "\n",
    "    def __init__(self, n_splits=3, test_period_length=1, shuffle=False):\n",
    "        self.n_splits = n_splits\n",
    "        self.test_period_length = test_period_length\n",
    "        self.shuffle = shuffle\n",
    "        self.test_end = n_splits * test_period_length\n",
    "\n",
    "    @staticmethod\n",
    "    def chunks(l, n):\n",
    "        for i in range(0, len(l), n):\n",
    "            yield l[i:i + n]\n",
    "\n",
    "    def split(self, X, y=None, groups=None):\n",
    "        unique_dates = (X.index\n",
    "                        .get_level_values('date')\n",
    "                        .unique()\n",
    "                        .sort_values(ascending=False)\n",
    "                        [:self.test_end])\n",
    "\n",
    "        dates = X.reset_index()[['date']]\n",
    "        for test_date in self.chunks(unique_dates, self.test_period_length):\n",
    "            train_idx = dates[dates.date < min(test_date)].index\n",
    "            test_idx = dates[dates.date.isin(test_date)].index\n",
    "            if self.shuffle:\n",
    "                np.random.shuffle(list(train_idx))\n",
    "            yield train_idx, test_idx\n",
    "\n",
    "    def get_n_splits(self, X, y, groups=None):\n",
    "        return self.n_splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to first run `sklearn_gbm_tuning.py` to perform gridsearch and store result (not included due to file size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-27T16:38:10.626811Z",
     "start_time": "2018-12-27T16:38:10.446215Z"
    }
   },
   "outputs": [],
   "source": [
    "gridsearch_result = joblib.load('gbm_gridsearch.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV object has several additional attributes after completion that we can access after loading the pickled result to learn which hyperparameter combination performed best and its average cross-validation AUC score, which results in a modest improvement over the default values. This is shown in the following code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Parameters & AUC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:17:46.468214Z",
     "start_time": "2018-10-23T15:17:46.461464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "learning_rate             0.0100\n",
       "max_depth                 9.0000\n",
       "max_features              1.0000\n",
       "min_impurity_decrease     0.0100\n",
       "min_samples_split        10.0000\n",
       "n_estimators            300.0000\n",
       "subsample                 0.8000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(gridsearch_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:17:46.637359Z",
     "start_time": "2018-10-23T15:17:46.628346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.6853'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{gridsearch_result.best_score_:.4f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test on hold-out set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:17:50.192859Z",
     "start_time": "2018-10-23T15:17:50.188776Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = gridsearch_result.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:17:50.884305Z",
     "start_time": "2018-10-23T15:17:50.735152Z"
    }
   },
   "outputs": [],
   "source": [
    "preds= best_model.predict(test_feature_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:17:51.168586Z",
     "start_time": "2018-10-23T15:17:51.153447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6622470619909608"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_true=test_target, y_score=preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect global feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:13.292514Z",
     "start_time": "2018-10-23T15:18:12.371138Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(best_model.feature_importances_, index=test_features).plot.barh(figsize=(8,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV Train-Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:19.870907Z",
     "start_time": "2018-10-23T15:18:19.839284Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 40 columns):\n",
      "mean_fit_time                  576 non-null float64\n",
      "std_fit_time                   576 non-null float64\n",
      "mean_score_time                576 non-null float64\n",
      "std_score_time                 576 non-null float64\n",
      "param_learning_rate            576 non-null object\n",
      "param_max_depth                576 non-null object\n",
      "param_max_features             576 non-null object\n",
      "param_min_impurity_decrease    576 non-null object\n",
      "param_min_samples_split        576 non-null object\n",
      "param_n_estimators             576 non-null object\n",
      "param_subsample                576 non-null object\n",
      "split0_test_score              576 non-null float64\n",
      "split1_test_score              576 non-null float64\n",
      "split2_test_score              576 non-null float64\n",
      "split3_test_score              576 non-null float64\n",
      "split4_test_score              576 non-null float64\n",
      "split5_test_score              576 non-null float64\n",
      "split6_test_score              576 non-null float64\n",
      "split7_test_score              576 non-null float64\n",
      "split8_test_score              576 non-null float64\n",
      "split9_test_score              576 non-null float64\n",
      "split10_test_score             576 non-null float64\n",
      "split11_test_score             576 non-null float64\n",
      "mean_test_score                576 non-null float64\n",
      "std_test_score                 576 non-null float64\n",
      "rank_test_score                576 non-null int32\n",
      "split0_train_score             576 non-null float64\n",
      "split1_train_score             576 non-null float64\n",
      "split2_train_score             576 non-null float64\n",
      "split3_train_score             576 non-null float64\n",
      "split4_train_score             576 non-null float64\n",
      "split5_train_score             576 non-null float64\n",
      "split6_train_score             576 non-null float64\n",
      "split7_train_score             576 non-null float64\n",
      "split8_train_score             576 non-null float64\n",
      "split9_train_score             576 non-null float64\n",
      "split10_train_score            576 non-null float64\n",
      "split11_train_score            576 non-null float64\n",
      "mean_train_score               576 non-null float64\n",
      "std_train_score                576 non-null float64\n",
      "dtypes: float64(32), int32(1), object(7)\n",
      "memory usage: 177.8+ KB\n"
     ]
    }
   ],
   "source": [
    "results = pd.DataFrame(gridsearch_result.cv_results_).drop('params', axis=1)\n",
    "results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:22.691680Z",
     "start_time": "2018-10-23T15:18:22.679156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_min_impurity_decrease</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>...</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>split10_train_score</th>\n",
       "      <th>split11_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107.0407</td>\n",
       "      <td>4.7653</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6271</td>\n",
       "      <td>0.6295</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.6336</td>\n",
       "      <td>0.6279</td>\n",
       "      <td>0.6295</td>\n",
       "      <td>0.6332</td>\n",
       "      <td>0.6280</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>113.1462</td>\n",
       "      <td>2.6408</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>0.6261</td>\n",
       "      <td>0.6259</td>\n",
       "      <td>0.6299</td>\n",
       "      <td>0.6370</td>\n",
       "      <td>0.6345</td>\n",
       "      <td>0.6336</td>\n",
       "      <td>0.6268</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>309.6415</td>\n",
       "      <td>8.1426</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6436</td>\n",
       "      <td>0.6445</td>\n",
       "      <td>0.6472</td>\n",
       "      <td>0.6410</td>\n",
       "      <td>0.6439</td>\n",
       "      <td>0.6469</td>\n",
       "      <td>0.6497</td>\n",
       "      <td>0.6464</td>\n",
       "      <td>0.6445</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>329.9059</td>\n",
       "      <td>7.1890</td>\n",
       "      <td>0.0222</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6421</td>\n",
       "      <td>0.6435</td>\n",
       "      <td>0.6464</td>\n",
       "      <td>0.6431</td>\n",
       "      <td>0.6451</td>\n",
       "      <td>0.6472</td>\n",
       "      <td>0.6478</td>\n",
       "      <td>0.6440</td>\n",
       "      <td>0.6446</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106.8347</td>\n",
       "      <td>3.4506</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6247</td>\n",
       "      <td>0.6265</td>\n",
       "      <td>0.6296</td>\n",
       "      <td>0.6262</td>\n",
       "      <td>0.6348</td>\n",
       "      <td>0.6297</td>\n",
       "      <td>0.6372</td>\n",
       "      <td>0.6337</td>\n",
       "      <td>0.6279</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       107.0407        4.7653           0.0150          0.0014   \n",
       "1       113.1462        2.6408           0.0190          0.0042   \n",
       "2       309.6415        8.1426           0.0220          0.0029   \n",
       "3       329.9059        7.1890           0.0222          0.0018   \n",
       "4       106.8347        3.4506           0.0158          0.0020   \n",
       "\n",
       "  param_learning_rate param_max_depth param_max_features  \\\n",
       "0              0.0100               3               sqrt   \n",
       "1              0.0100               3               sqrt   \n",
       "2              0.0100               3               sqrt   \n",
       "3              0.0100               3               sqrt   \n",
       "4              0.0100               3               sqrt   \n",
       "\n",
       "  param_min_impurity_decrease param_min_samples_split param_n_estimators  \\\n",
       "0                           0                      10                100   \n",
       "1                           0                      10                100   \n",
       "2                           0                      10                300   \n",
       "3                           0                      10                300   \n",
       "4                           0                      50                100   \n",
       "\n",
       "        ...        split4_train_score  split5_train_score  split6_train_score  \\\n",
       "0       ...                    0.6271              0.6295              0.6300   \n",
       "1       ...                    0.6250              0.6275              0.6261   \n",
       "2       ...                    0.6436              0.6445              0.6472   \n",
       "3       ...                    0.6421              0.6435              0.6464   \n",
       "4       ...                    0.6247              0.6265              0.6296   \n",
       "\n",
       "   split7_train_score  split8_train_score  split9_train_score  \\\n",
       "0              0.6325              0.6336              0.6279   \n",
       "1              0.6259              0.6299              0.6370   \n",
       "2              0.6410              0.6439              0.6469   \n",
       "3              0.6431              0.6451              0.6472   \n",
       "4              0.6262              0.6348              0.6297   \n",
       "\n",
       "   split10_train_score  split11_train_score  mean_train_score  std_train_score  \n",
       "0               0.6295               0.6332            0.6280           0.0042  \n",
       "1               0.6345               0.6336            0.6268           0.0059  \n",
       "2               0.6497               0.6464            0.6445           0.0028  \n",
       "3               0.6478               0.6440            0.6446           0.0019  \n",
       "4               0.6372               0.6337            0.6279           0.0050  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get parameter values & mean test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:23.032859Z",
     "start_time": "2018-10-23T15:18:23.011012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 8 columns):\n",
      "learning_rate            576 non-null object\n",
      "max_depth                576 non-null object\n",
      "max_features             576 non-null object\n",
      "min_impurity_decrease    576 non-null object\n",
      "min_samples_split        576 non-null object\n",
      "n_estimators             576 non-null object\n",
      "subsample                576 non-null object\n",
      "test_score               576 non-null float64\n",
      "dtypes: float64(1), object(7)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores = results.filter(like='param').join(results[['mean_test_score']])\n",
    "test_scores = test_scores.rename(columns={c: '_'.join(c.split('_')[1:]) for c in test_scores.columns})\n",
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:25.491806Z",
     "start_time": "2018-10-23T15:18:25.489879Z"
    }
   },
   "outputs": [],
   "source": [
    "params = test_scores.columns[:-1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:25.708134Z",
     "start_time": "2018-10-23T15:18:25.685694Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_score</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>max_depth</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>max_features</td>\n",
       "      <td>sqrt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>min_impurity_decrease</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>min_samples_split</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_score              parameter  value\n",
       "0      0.6640          learning_rate 0.0100\n",
       "1      0.6640              max_depth      3\n",
       "2      0.6640           max_features   sqrt\n",
       "3      0.6640  min_impurity_decrease      0\n",
       "4      0.6640      min_samples_split     10"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores = test_scores.set_index('test_score').stack().reset_index()\n",
    "test_scores.columns= ['test_score', 'parameter', 'value']\n",
    "test_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:25.798060Z",
     "start_time": "2018-10-23T15:18:25.791396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4032 entries, 0 to 4031\n",
      "Data columns (total 3 columns):\n",
      "test_score    4032 non-null float64\n",
      "parameter     4032 non-null object\n",
      "value         4032 non-null object\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:26.419366Z",
     "start_time": "2018-10-23T15:18:26.410701Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_test_scores(df):\n",
    "    \"\"\"Select parameter values and test scores\"\"\"\n",
    "    data = df.filter(like='param').join(results[['mean_test_score']])\n",
    "    return data.rename(columns={c: '_'.join(c.split('_')[1:]) for c in data.columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Test Scores vs Parameter Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV result stores the average cross-validation scores so that we can analyze how different hyperparameter settings affect the outcome.\n",
    "\n",
    "The six seaborn swarm plots below show the distribution of AUC test scores for all parameter values. In this case, the highest AUC  test scores required a low learning_rate and a large value for max_features. Some parameter settings, such as a low learning_rate, produce a wide range of outcomes that depend on the complementary settings of other parameters. Other parameters are compatible with high scores for all settings use in the experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:27.254261Z",
     "start_time": "2018-10-23T15:18:27.230355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 7 columns):\n",
      "learning_rate        576 non-null object\n",
      "max_depth            576 non-null object\n",
      "max_features         576 non-null object\n",
      "min_samples_split    576 non-null object\n",
      "n_estimators         576 non-null object\n",
      "subsample            576 non-null object\n",
      "test_score           576 non-null float64\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 31.6+ KB\n"
     ]
    }
   ],
   "source": [
    "plot_data = get_test_scores(results).drop('min_impurity_decrease', axis=1)\n",
    "plot_params = plot_data.columns[:-1].tolist()\n",
    "plot_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:37.887464Z",
     "start_time": "2018-10-23T15:18:34.576268Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ulC9fnoCAADp16kRkZKROv/uSx5k2bRobNmygW7dudO3alTFjxuh0f7n/c5U85yUplUo6duzIkCFDHrn9o/7GSyurIAiC8OKIFHdBEISXSE9PjxYtWrB371727duncwMO4ObmRnx8PC4uLpp/mzZtYvfu3WRlZbFlyxamT5/OZ599RqtWrTSDhj3rtFylsbGxwdrampSUFE1ZHBwcmD59OpGRkVy7do2pU6fi4+PDp59+ysaNG/Hz8yMkJATgodPHpaWlMW/ePK3UfJAeRBgZGWFjY4OzszN6enqa/vcABw8e5J133sHZ2Rm5XM758+c161JTU7l+/bpOoFHEzc2NhIQErXO7YcMG9uzZ80TnZdOmTcjlclq0aKGzbvr06aSkpNCrVy/mzJnDuHHj2LZt2xPtv6SS/eMvXLiAoaEhTk5OmuC3ZLp7fHy85ueHnXt3d3et8wZw7ty5B563pylnpUqVNEFv+/btuXjxIhs2bKBp06YP7PLwJB62z7i4OK3uB2FhYXh6egLSdyApKUnrO1A0VsDzcvz4cW7fvs2SJUsYOHAgjRo14saNGw/9G121ahWjR4/m66+/pl27dpryP83ftZubG3FxcVqf8cCBA2zYsEFn20f9jQuCIAgvlwjQBUEQXrLAwEDWrl2LtbW1VspqkX79+rFjxw4WL15MXFwcK1euZN68ebi4uGBoaIiJiQm7du0iISGBw4cPM378eACtQeKep379+vHbb7+xZ88e4uLiGDt2LCdOnMDd3R0LCwtWrFjB77//Tnx8PMeOHSMyMlKTkm5qakpsbCx37tzR2e/bb7+Nu7s7H374Ibt37yYhIYHQ0FBGjRqFvr4+rVq1wtLSki5dujBx4kTOnz9PWFgY06dP56233sLCwoIePXowfvx4Tp48qRlcz8HBgcaNGz/ws2zbto2///6b69eva1r7S6Zz3y8vL4+UlBRSUlKIi4tj9erVTJ06lWHDhpU6vdq1a9cYP3484eHhREdHs2vXLk3/cjMzM9LT04mNjX3sbge//vorx44d4+zZs0yaNIk+ffpgbGyMp6cnRkZGzJ8/n/j4eBYuXKgVUBUFx2FhYVqDfQF8+OGH7Nixg6VLlxIbG8uiRYvYu3cvffv2fawylWbChAmcP3+ew4cPM2fOHM0o8gAVK1akTp06LF68mA4dOjz1MUp62D5zcnIYO3YsUVFRrFq1it27d2s+W79+/fj777/ZuHEj169f57fffmPjxo24u7s/l3KBNABiTk4OISEhJCQksHr1as1AcQ97z759+4iPj+f06dOawdie5u86KCiICxcu8OuvvxIbG8u2bduYPn26Zt72+7+HD/sbFwRBEF4ukeIuCILwkjVu3BilUllq6zlIfaynTZvG7NmzmTZtGg4ODkyaNInmzZsDEBwczNSpU1m+fDmOjo4MGTKEWbNmcfnyZU0r4fM0aNAg8vLyGDt2LJmZmVSvXp2FCxdq+rPOnDmTX375hXnz5mFlZcX7779P165dAejduzffffcdcXFxrFu3Tmu/hoaG/P3338yePZvJkydz69YtzM3NadasGUuXLsXExASA77//np9++on+/fujr69PmzZt+OabbwBp7umpU6cyfPhwCgoKaNKkCYsXL9Z0B7hf3bp1mTJlCr///jvBwcE4OjoyZcoUmjZt+sDPv337ds2I4RYWFri5uTFmzBidNPkiEyZMYPz48XzwwQcUFhbSsGFDzejdjRo1wsXFhY4dO2oGLXuU/v378/3335OWlkb79u356quvACn9ePz48fz2228sXbqUVq1aERQURFpaGgDe3t40btyY3r1764yi7+vry88//8ysWbOYOnUq7u7uzJw5k4YNGz5WmUoTFBTExx9/jEKhoFevXrz//vta69u1a8elS5do2bLlUx/jfg/aZ6VKlbCzs+Pdd9/F3t6e6dOnU7duXUAaAC41NZWZM2eSkpJClSpVmDt3rs6Udc/C399fM3J6fn4+1apVY+zYsXz//fc6GSNFpkyZwrhx42jfvj0VKlSgZ8+e6OvrEx4eTrNmzZ7o+E5OTsyfP5/p06fz559/Ym9vzxdffEHPnj0B3e/hw/7Gn3b8CkEQBOHpyNQvIidSEARBEAShhBkzZnD9+nWmT5/+Qve5du1a5s6dq5ktQRAEQRBeJ6IFXRAEQRCEFyYiIoKIiAhWrFihmae7LO5TEARBEMoC0QddEARBEIQXJiwsjB9//JGuXbvSoEGDMrtPQRAEQSgLRIq7IAiCIAiCIAiCIJQBogVdEARBEARBEARBEMoAEaALgiAIgiAIgiAIQhkgAnRBEARBEARBEARBKANEgC4IgiAIgiAIgiAIZYAI0AVBEARBEARBEAShDBABuiAIgiAIgiAIgiCUASJAFwRBEARBEARBEIQyQATogiAIgiAIgiAIglAGiABdEARBEARBEARBEMoAEaALgiAIgiAIgiAIQhkgAnRBEARBEARBEARBKANEgC4IgiAIgiAIgiAIZYAI0AVBEARBEARBEAShDBABuiAIgiAIgiAIgiCUASJAFwRBEARBEARBEIQyQATogiAIgiAIgiAIglAG6L+InapUKn788UeuXLmCoaEhEydOxMXFRbP+wIEDzJkzB7VaTY0aNRg7dizp6el88803ZGVlYW1tzcSJEylfvvyLKJ4gCEKZIepLQRCExyfqTEEQ3nQvpAU9JCSEgoICVq9ezVdffcWUKVM067KysggODmbevHmsXbsWBwcH0tLSmD9/PnXr1mXlypW8//77/PLLLy+iaIIgCGWKqC8FQRAen6gzBUF4072QAP3MmTM0bdoUAF9fX8LCwjTrzp49i6enJ1OnTqVv377Y2tpiY2PDtWvXaNasGQB16tThzJkzL6JogiAIZYqoLwVBEB6fqDMFQXjTvZAU96ysLMzNzTWv5XI5CoUCfX190tLSOHHiBBs3bsTU1JSgoCB8fX3x9vZm7969VK9enb1795KXl/fI44SGhiKTyV7ERxAE4T9KJpPh6+v70o4n6ktBEF5nos4UBEF4PI9bX76QAN3c3Jzs7GzNa5VKhb6+dChra2t8fHyws7MDwN/fn/DwcAYPHsxPP/1EUFAQzZs3p2LFio88jomJCd7e3i/iIwiC8B8VHh7+Uo8n6ktBEF5nos4UBEF4PI9bX76QFPc6depw8OBBAM6dO4enp6dmXY0aNYiMjCQ1NRWFQsH58+fx8PDg9OnT9OjRg+XLl+Pi4kKdOnVeRNEEQRDKFFFfCoIgPD5RZwqC8KZ7IS3orVq14siRI/Tu3Ru1Ws2kSZP466+/cHZ2JjAwkK+++oqBAwcC0LZtWzw9PTEyMmLEiBEA2NvbM2nSpBdRNEEQhDJF1JeCIAiPT9SZgiC86WRqtVr9qgvxtMLDw0X6kSAIz9WbWq+8qZ9LEIRX602tW97UzyUIwqvzuPXKC0lxFwRBEARBEARBEAThyYgAXRAEQRAEQRAEQRDKABGgC4IgCIIgCIIgCEIZIAJ0QRAEQRAEQRAEQSgDXsgo7v91yRl5bLt4E0tjA9rXqoSxgfxVF0kQBEEQ3ghnks9w7tY56lSog5+936sujiAIglDGnLt1jjPJZ6hlV4t6Feu96uI8MRGgP2cxt7PpPPswGXkKAJYci2XD0MbI9WQA5BQoKFCosDY1fIWlFARBEISyLV+ZT3ZhNjbGNppli8MWM/3MdM3rEfVG8F71915F8QRBEIRXrFBZSHpBOrYmtpplKyNWMulE8VSKn9X5jIE+A19F8Z6aSHF/zpYdj9ME5wDnE9I5fO02AL/vv0adCbvxm7CbocvPkFeofFXFFARBEIQya13kOlqsbkHz1c0ZvGswGQUZACwMW6i13cKLC0t7uyAIgvCG2xO3h8C1gbRc05K+W/uSnJ0M6F4XFl1cxOs2q7gI0J8zVSlfAJVazZWkTH7ecYW8QhVqNWy7mMSKE9dfQQnfYCrVqy6BIAjCo6nEw9mHSclJ4afjP5FVmAXAsZvHWHRxEQAqtXY9r+b1uukSBEEQno6yxLUzT5HH6KOjSctPA+Di7YvMPDsTQCcYV/H6xQciQH/Ogho4Y2ZY3Ofcu5IlTT1suZKcqbPtlSTdZcJTOL0IfnaHSZVg+wgRqAuCUDalxcGitjDeBuY1heTLr7pEZVJMegwKtUJr2dW7VwHoV6Of1vIPanzwsoolCIIgvALnbp2j88bO+C31Y8juIdzJvUNSdhKZBdpxVGRaJKB7Xfig+gfIZLKXVt7nQfRBf8487C3Y8XkzNl+4gZWJAV18HdCX69HQ3QYjfT3yFcXBY3MvO53338nK58/DMSTezaW9TyVa16j4Mov/+km5Alu+hKJWlBPzoKIP+N3rk5iXAVnJUN4DXrM/ztfZ3by7GMoNMTUwfdVFEcqYuzkF6Mv1MDd6DS8/V7ZD2HqwdIBGn4C5bh3+UFs+h+vHpJ+TLsA/g2HI4edfToCcVMi7CzbuL2b/L1BN25pYGVmRnp+uWdbEoQkAg2sNpkb5GpxPOY+vvS9vVX7rVRVTEAThP61QVcjdvLvYmT7htfA++cp8ll1eRnhqOA0qNeDdqu9qAmqlSsk3B78hKTsJgCM3jjDt9DR+avITzhbOXM8szkYuuk58UOMDvGy8CE0OxcfWh6aOTZ+pfI8jPiMeC0MLrI2tn8v+XsM7pLJl+8Wb/HUkFgN9GR8396BJVVucbEwZ2sJDazt7C2MW9avHr7sjycgrpHc9Z9r5VNLaRq1W896fJwm/KfW123TuBrP6+NGxduWX9nleO4ln4P4Ux4TTUoAeuhS2fwuFOWDnDUFrwdrplRTzvyJfmc93h74jJC4EI7kRH9X+6LUbmEN4MRRKFd+uu8DGc4no6+kxqJkb37Sp9qqL9fgubYS1JZ7KX90FQ46A3hMkoiWc0X6ddBEUBaD/nAcNPRgM+6eCqhCcGkLfVWBS7vke4wUyNTBlbuBcZoTOIDknmfbu7enl1UuzvrFDYxo7NH6FJfxvUKvVpC5aRMb2HRg4OGD32acYub9+D3wEQXhy2YXZzAydSeitUGrb1Wa433CsjKw06/fH72fs0bGk5qXibePNjJYzqGRe6SF7fLDRh0ezPXY7ADtjd5Kcncwnfp8AkJKbognOi1y8fRE9mR6zAmfxy+lfiEmPoYVTCz6u/bFmm4aVGtKwUsOnKs+TSM9PZ/je4Zy9dRZ9PX2G1BrCR7U/eub9vrEB+k9bL7P4aCwqNbTytmfe+/7P/Rhn4tIYuiKUoq4OJ2NSCfmyOS7lzUrdvrGHLY09bEtdB3DpRoYmOC+y7kzCUwfoGXmFhMal4VnBgsrWJlrr8gqVHLp6m8S0HFacvE58ai7v+FTkpy4+mBi+RtPCOTUAmR6U7Jfo0hjy0mHbN6DIlZalhMO+SdB17qsp53/Emitr2B23G4A8ZR4zQmfQwrEFHuU8HvFO4U234WwiG84mAlCgVDFnXxQB1eyp62LziHeWEedXar++dRlunAXHurrb5mfC1q8hcjvYekK7aVDZF1zekpYVcfB//sH5nSjY+xOaB5fxx+HYHAj44fke5wXzsfNhYZvHGwDu0p1LzD8/n4yCDLpX7U7HKh1fcOmK3Vm8mNu/zwW1mvKDBmE7eNALOU5+TAyFN25gWrcuesbGL+QY90tbuoxbwdMAyAsLI/fCBTx270Km/8beOgqCcM+oQ6PYG78XgIjUCOIz45nfaj4ABcoCRh8Zzd38uwCEp4bzy5lfCG4e/MTHyVXksjNup9ayTVGbNAG6vak9ThZOxGfGa9bXrSBdd92t3JkdOPvJP9xzkpKTws+nfubsrbMAKFQKZp+bzTtu7+Bs6fxM+34j+6DvCLvJgkMxFCrVKFVqdlxKZvzmS0+9P6VKzbaLN5kRcpWLCcUpdyHhyZQch6BQqWb/lZTH3u/dnAKOXLvN3ZwCAMqZGepkYZc3f7qbt1OxqTSevJd+f52i6c/7WHo8TrPuZnouAdP2M2jJaX7cfJnI5CxyC5VsCE1k5t6rT3W8V6Z8Fei2AMq5gaktNPsWfN6FjBvFwXmROyU+m0oJhXkvt6z/AVfTdL8/RX1Hhf+20sbciHidxuEwvf/hqgzMype+bcg4uLBKelBUOk8+AAAgAElEQVSYcApWvyfVOR1/A8+2YGgOrk2h2x/S9nkZEL0fsm49WZkK8+Dscjg4DW5fk5alRqOTVXTn2pPtt4zKVeTqDP6Tnp/OwJ0D2Re/jzPJZxh1eBQH4g+8lPJkHTvGrSlTUWVkoMrMJOWXX8jct/+5H+fWL78S/U474gcMJKpVa/KjY577MUqTvmmT1mvFzZvkhYe/lGMLgvBq7Yvfp/X66I2jmvo3JTdFE5wXKe3+73EY6BlgbmCutazk1Jp6Mj1+bfErte1qY2ZgRlvXtnxZ90vylflag8a9bFuit9B6fWt2xO7QWXcm+Uwp73gyb+Rj0PkHonWW/Xv+BmM61niq/Y1Yf4F1ZxIAmLEnkt+D6tC2ZiXcbXVbyt3uW5aUnsemc4mYGMrp4ueApbEBAHvCkxm2IpS8QhXGBnrM6lOHVtUrMLCJGwsOSRdfW3MjnVT5klQqNatOxXMi5g4+Dlb8r5ErhvrSM5fgHVfIzJcG2VGq1Py8PYIedR0xNpCz+EgsN9JLD05Px6Y+4dkpA3zelf6VZOsFNlUgNap4WbX20v9nFkPIj1Irl08P6Djz+bdi/Uc1cWjCP9f+0bw2khtRr2K9V1gioaxo4WXHn4eLAwt9PRlNHpJRVOY0+QKu7ZbGtABoMATKuZa+bdwR7dfp8XA3TuoP3ne19rrYw7CyD+RngJ4BdJ4DtXvxWJZ1Kz7WwWDotw2cG0np7Llpxdt5tX+8/ZUBp5JOcTrpNDVsa9DMsRkgtVJ8e/BbTiefxtHckfGNx2vqlRM3T2hGey+y5/oemjs1f+Flvbt6danLVNnZ5F28iGmD+lgEBDzTMQqTkrizsDiTQJGSwp3586g8deoz7fdxKO7e1VmmVogZCAThv+BhM2RUNquMu5U70enF8VYThyao1Co2R23mXMo56tjXoYN7B53B2QqUBWyP2U5SdhKtXFrhbu3Ol3W/ZMLxCSjVSozlxnxW5zOt93jZeLGs3TJAmvd83LFxbI3eipmhGZ/6fUpPr54P/SzXM66z4eoG9GR69PDsUWoqfmJWIhuvbUQuk9OtajfsTe0ByCrIYnn4cmIypDT6tq5tUalVTDs1DYVKobOfouM9qzcyQLezMNJZVs7U4Kn2dTsrnw2hCZrXKjUsOBRD25qV6OLnwL4rt9h2MQk9GfSu70zTqsU3nPGpOXScfZi7OYUALD4Sy9ZPm2JiKGfClsvkFUpp2XmFKiZsuUyr6hX4vn11evg7kZiWS0P38g9NN5+26wq/75cC0E3nbnAlKZPgHrUBSMnK19o2M19BXqESYwO5pjylKVQ85ylrlApIi4VyLiB/ut/BU9HTg/fWw94JUouSd0d46zOpLFu+KE6JP78SKtaCRkNfXtneYK1dWzMydyTrItdhaWjJUN+h2Jq8RkGY8MI0rWrHlG4+LD4ai7GBnE8DPR7YHahMsvWAT89JAbGlA1SoLi1XFkr/DEsMiOhQR0qBL2JmD1YPGP9i9xgpOAepz/jO76Bmd5A/4vKccEb7QYAiD07Ol1rl//cv7J8C2begdh+o1ePJP28pchW53Mq5hbOF8wsZEXd5+HKmnJyieT3IZxCf1vmUaaencTr5NAAJWQmMPDiSne/uRF9Pv9Q0wmdNLXxceia6g2Dmx8Rw4+uvAUj9+2/svvoS20HFae+FybeQW1uhZ6R7n1IaZWqqzswkipTbz1BqXbnnz5MycxaKO3ew6tKZ8v36AaDOzdXZVs/CXGeZIAj/HUnZSZQ3Ls+sgFkEnw4m+m40zRybMbzOcKafns6Sy0sAWBe5jqi7UXxe93Ot93+y5xOO3ZQGS51/YT4LWi+gu2d33qr8FlfSruBr58u1u9cYuHMgGQUZvOv5rlYAviZyDZuipOye9Px0Jh6fSMNKDTX1fqGyUAr09aWuQDezbtJ7S28yC6WMvfVX1/NP53+0WumTspPoubknGQXStXj1ldX80+kfrI2t+Xzf55xIOgHA1uitZORn0NWjq04GQUkVzCo8/Qm+540M0G1MdVtD/ZxLHyAnr1DJHwejOXs9jXpuNgxs4q5phQYo7RZE795CA7kevwfV5WZ6Lvp6ejoPBtaeSdAKhqNvZ7M7PJlOtSuTnKEdQCdnFLdoe1awwLOChdZ6hVLFhcR0KlkZU8lK6k++5nSC1jb/nE1kUjcfDOR6dPNzYPruSM26gGr2WN87Lz38HVkXmoBSpRuMx9zO0ln21BJOw+r3IfOGdIPaYzG4vsSBfWzc4N1F2stuXtDurw5SP9IiyZch57bUCvUyHyi8QYK8gwjyDnrVxRDKoN71neld/+UETy+EoSlUbVX8+uQC6SFgfpaUxdNpFugbwdvjITMJru2R6qGOMx9cn2RqD35DTioo83UD9IwbkHwJHOuBiXXpFyfZvWtXpVrQZ8VTf8zS7I7bzdgjY8kszMTV0pXZgbNxsXTRrL+de5uI1Ahqlq/51KPY/n3pb63Xy8KXMcx3GJfvaE9Hdyv3Frdzb1PRrCLVbKrxYY0PWXJ5CUq1kroV6tLbq/dTHf9JGTg66iwrjI/Xep22bDm2gwahSEkh4ZPh5J4/j56lJRVH/4BVR6mvvFqtJufUKVAoMG3QAJm8+MG8kbc3Rl5e5F+5ollm1aXzQ8ulys4mLyICo6pVkVtaapZnHTnC3TVr0TMzo3z/DzHy8ECZlcX1gYNQZUo3r7emRCC3tsa6SxeUpbWgFxQ8xpkRBOF1J0Om04re/d/uXL17FRtjG8a/NZ5ZAbO01q+NXKv1ek3kGq0A/UrqFU1wDtIo8MvDl1O3Ql0qmVeiknkl7uTe4eOQj8lTSnHRhOMTKG9SnkDnQACd64EaNeGp4ThbOrPw4kIWXFhAgaqAbh7dGNVgFFtjtmqCc4DUvFR2xe6id7Xi68SW6C2a4Byk69muuF00cWiiCc6L/HP1H3p69aStW1u2Rm8t9dx52XiVuvxJvJEB+qWb6TrLolNKDzxH/XORDaHSwEX7rqRw424uE7v4aNaXNzeiVz0nVp6ULrpyPRmDm1XR2kdRwHw/vVJuoIqWdfGrrNknQBdfhwd+nvjUHIIWnuB6ag56MvjibU+GB1bFxsyA2yVayq1MDJDfa9X4JMCD8uZGHIxMoVolCwY1LR551d/VhjUfNWTmnmsciNTuM5+neI5ziG/9SgrOQWrJ2fwZDJdaQbgTBVe2Sa1K1To8urXoUQpzIfJePxDPtmBQ+u8EpwYgN5JugIu4SWmUbPoEzi6VfrZxl9JFLZ9uRErh8RxMOMjCiwtRqBS85/0e7dzbveoiCYIUcMcellLY7R8w0vydKGkgyqIbmAuroZKvlI1jVl7K4FEWFgfmWbek1vLEUHBrCm//CEYWUjebI78V77daezC8L7MgdAls/hzUSqn/ep+VUr3l1hxi7vW3NjCFBs8+cmxpCpQFjDs2TnOTE5sRyy+nf2FGwAxAGnX3u0PfUagqxEhuxPTm0zUp5jezbrI7bje2Jra0cmmFwUMefOrJ9Ep9Xb9ifWIzYjXLXS1dqWBa3ELxpf+X/K/G/8gqyMLVyvWB+z936xzzzs/TtMp0q9rtic7D/Qwq6V4fZIaGqPOKH7jLjKUH9ymzZpN7/jwAqowMbo79EfMWLdAzMuJ6/wHknJaujUbe3rgsXYrcXPoOyGQynBf9yZ1FiyhMvIFl2zZYtm2LWqUi+8hRFCkpmLdsgX45qREi+/hxEj4ZjiorC5mJCQ7TgrEIDCQn9CzxgwZrWuOz9uyhyq6d5J47pwnOi2QdOIB1ly7IjIx0WtH1rZ/PFEKC8EqkJ8Dlf8HMDqp3kh6oPsjRWXB+NZjbQ8D34FDKgKDPi6IAru4ERb50D2v06jNVPKw8uJpe3K/cRG6iGVMoNS+VMUfHEPJuiFadbmZgRm6J8Z/u71t+fx1f2rKTSSc1wXmRQwmHNAF6/Yr1+TfqX806Az0D6tjX4dLtS8wInaFZviZyDbXta2OirxsPmBloX2NL28ZIboSZgRkGegYUqoobW4seQI97axwe1h4sDltMeoF23GlhoN3I+jTeyABdodRtGS5tmVqtZvP5G1rLNp29oRWgA0zq6kNgtQpcS8mipZc9XhVLP/H7Im4RlpjOWx621HUpR09/J5Ydj+N2lvTE2bOCOW97SzcV4zrVxMnGlDOxadRxKcfApm4ApGUXcCAyhYpWxjR0lwYgmr33GtdTcwApxf63PVfpWc+JEW2r8fGyUAqUKvRkMOKdaujdewIgk8no28CZvg1Kb62q62LD+w1ddAJ0lfo5prjfvm/AiNQo6eYg4RT83bE4SPZqr9vak5ch9du0qwZ6jxhVPi8DFr4Nt++1MNhVg4Eh0s3v/SwqQK+lsGe81FJV531pSrab54uDc5DS4o//Dq0nSK9VKmnQuftvnIWnFpMew2d7P0OhlvrwjDg0gsrmlfG1933FJRPKktTsAg5E3sKxnCn1XF/CiO/Jl2BxB8i9Nx5Hky+kYPp+SRfRGYzt5nnt1yWD0fUDi4Pp21egIBu6zoPAMWBeQVpXqTY01u57h7IQdo2WgnOAgixpDI1BeyFoHYT/K7XCe3eUuhKVJvs27BwF8SfBuSG0HCXVm3Zej5UplJqXqjUnOUBMRvF4AsGngjU3MPnKfKafkQL0K6lX+N/2/5GjkK5f666uY1Gb+7KaShjgM4Dxx8ZrXver0Q+5npwv/b8kT5nH4cTDVLGuwqj6o3RS7G1NbDXdaZKyk9gRswNLI0vaurbF1MCU1LxUBu8erLl5vHj7IjbGNrRwagFIfQaXhy8nV5HLu57vUsuu1iPPi+U7bUldupT8ewOnGVWtikX79tz+7d4DF5kMu2HDpPNyVft6qM7JoTAxkYKYGE1wDpAfHk76v5uw6dtXs0y/fHkqfPON1vsTPv2UrJA9AOhZWeG6YjlGVaqQPHkKqiypQUKdm0vSxJ+wCAwkY+tWrVR5ZXo6WQcPYeJbG2QySo54a+QuNULYfjyElF9+1Sw3bdgQg8pi2lfhNZUUBovaSHUowJkm0G8LOqMzgzRN7657M18kI03p+8Ul3cBZrZbq8mcJqBX58GdruHlOem3tDIP2P3gQ0pekZHAOkKvUfliXmpfKnbw7VDSrqFn2qd+njD06FjVq9GR6DPcbrvWequWq0tyxOQcSpGuhkdyI96u/r7WNu5XuVI5uVm6anztV6cSNrBusv7oeSyNLPvP7DDtTO/Yn7Nd5X/idcIb6DmVVxCrNQ95qNtV42+VtrqVd49KdS5q+8svDl2tGivew9qCVSytMDUwZXGswc87NAaQHDkNrD9WUfaDPQOacnaNz3D8u/MHPzX/WWf4kXkiArlKp+PHHH7ly5QqGhoZMnDgRF5fiG4cDBw4wZ84c1Go1NWrUYOzYsWRlZfHFF1+Qk5ODoaEhwcHB2Nk93cT3Fsa6AV18ag5tfztI6+oV+DSwKvpyPWQyGfYWxiTeLf7SVbDSnb5EJpPxdvUKvM2D+xRM3h6uGZxu+u5Ipnb3oVc9Z3Z83oytF25iYiCnfa1KGBtIZTPU19MZAO5KUiY95h0lI08KWLr5OfBLL19upGv/UShVapLS8wj0rsDhkS0JjbtLTQdLHMtJ/eHUajVqNZpgHSCnQMGfh2KISM6kuacdPf2dyMjVTVUrLe39qXm9A2Hril97tpX6hh+brd2CfWUrpFyRbhYBLqyVWtsLs6WKKmhd8brShK0rDs4BUiIgbD3U7QdR+2DHSLgbDzW7StMdebaR/pVU2ujJRcsitt7LBrgJHm9D94Wv1ZzCL8rNrJso1UocLXTTPEujVClRocJATwoIjiQe0QTnRQ4mHPzPBeivur58Fum5hYz65yJ7wpPxsDdnYhcffJ0e3cKWnJHHuM2XOB+fTgM3G8Z0rK7pglPSpRvp9J5/XDPgZS9/J6a+++jA6Zns+6k4OAc4MgPqf6SbTePcSDcbx/3ewGQqlTS1o5UjGFtJo63H3DeyeOS9aWX05FKr+4PGwVDkSaPBl1RUN+kb6g6QWZpNw4ozjNJi4OJaUCnAvKLUGu9Q56Fvr2hWkSrWVYi6WzzoZtEAbiq1ijt5d7S2v50j9ZFeEbFCE5yDNADcuVvn8LX3RaFSsPrKakKTQ/G196V3td708OyBVzkvTiWdoqZtTRpUagBIrR0/NflJ6xgJmQn8FvobMekxNHdszse+H2OgZ0BMegx9t/bVDBy3KmIVK9qv4OTNk1otOyCNUtzCqQXp+ekEbQvS9CncHL2ZVe1XPTJNUc/EBLc1q8k6dBjUKsybNkVmaIh5kybkhV3E1N8fIw/pOm/evBm5Z4u7U+lXroSRhwc5oaE6+1WmpeksKynv8mVNcA6gSk8ndfFiKk2YQGGSdpcJxa1bqBUK9Cvo3r/oV7DH0MkJ+2++IWXmTNR5eZi91QibD/sBYDt4MGZNmpC+4R9MG9THslUrnX28bK9zffky/bI7kiXHYjE1kPN5K096+j9gDIw3RWEeGDxi+sGTfxQH5wBxh+89tGygu+1V7Wm/yLsL8SfAI7B4WdQ++PdTSL8uzcrR/U+pEehJRWwtDs4B7l6XGoyafP7g95QBVctV1QrOAbpW7YqfvR8Xbl/A106q54fvHU5CZgKBzoEMqT2EX1v+yt7re0nKTiLAKYC0/DRGHxmNkdyIIO8gvGy8GFp7KAsvLqRAVUALxxb08urFivAVbIraRDnjcgyrPYyPfT/WOna9CvWQy+Qo1cUDWTas1BALQwvWdVrHgfgDyPXkNHNoxrqr65h0YhIAcpmcKU2nsK7jOvbF70OuJ6elU0uM5FJ2xZDaQ2jt2prY9Fj8K/pjaWipddySxytSoHz2rkAvJEAPCQmhoKCA1atXc+7cOaZMmcLcudL801lZWQQHB7NkyRJsbGxYsGABaWlpbN68GU9PT7799lvWrFnDn3/+yciRI5/q+MYGuh/rTk4hd3IKiUjKRK6nx2dvVwVgdIfqfLbqLPkKFaaGcn5o7w1AzO1s1p9JwMRQTq96TtiaS78olUrNviu3iE/NIdC7Ak42puQVKll8JFbrePMPRtOrnjO25kZ88Jar1robd3OxMTPUBOua9xyI0gTnIM0bPLSlBx1rV+bQ1eJBYdxtzfBxsALA3sKYtjUrau1jzr5rKFRq+jd24+s20g3G8BVn2RMh3dRtvXCTO1kFlDMt5df/LPH50VlSKqaxtZQO1OFXMLWBuGPSXMFv/3jvGKWk0RctK8yDbV9JwTlIFdXusdB31YOPW6g7kA0FOVKa6poPoKjl5+wysKgsle1+rk2lgZ8yEu8tkEGtntJI7xs+goJ7KYDXQmDfZGhX4slY4hk49It0E+3/oTTAU8l1B34ubq2v878Hf47XhEqt4vvD37MlegsAgc6BBDcP1gTepVl6eSlzz88lX5HPu57vMqL+CNytdZ+SlrbsTfeq68tn8fOOCLZeuAlAWGIGHy87w6FvW6Ivf/gMnp+tOsvxaCkI3nA2kXylijl9dYPEufujNME5wOrT8Qxr6YFzed3BuZ6bxPumR1GrpIHb0hPApTG0GCl1obGoAL2XS33Qc9Kkv+/afaTU92XdpUDYwFR6KOjbV0qXT4st3u/DHjqWZGQB3h0gfHPxstp9Hrz99eNSGr1rE6kvOsDV3drbFI08m5Ukpc4POfjIYqjuq7eLprfRk+nRwb0DG69t1Kwrmof8/vdA8cjAwaeCWREhZU7tittFTHoMYxqNoZZdrUe2XqvVaj7Z8wlR6dIDg8g0abyVT+t8ypora7RGdQ9PDefYjWOlpr67WUqtMvvj92sN+KNQKdgavfWx+hHKDAywCGiptcykZg1MamrPGlN+4EBUeXlk7totBcVff4VMXx/LVq1I+W0Gqgyp/6PM2Bir9tKo++qCAhQpKehXrqyVMaAqZfA2VY60zLLdO9xdWXy9tGzTGpm+PuV69yJj+3ZNa79l+/aY1a8vla3/h1j37IkqOxuDCvban6V6dUyqV3/keXhZXuf68mXZeuEmM/dIrZ93KWTE+gv4OVlTtcKzp92WOfGnYOPH0jS6Lo2laXetHtBltNR7T6WUTbR3IsQfB8f6EDhamgmIEnWuTA9sqxa/VhTAhkGQfS8LNfaQ1IWp2/yHlzfjpvQQOOWK1FDU5IvS72ELc3SXlQHveb+nyWT6yv8rIlIjOJV0ihrla1CngnQNd7VyxdXKFaVKSbsN7biRLWUqX7t7DSO5EYNqDaKNq9RIFpkWSb8d/TQZWNtjtrO562Y+9v2Y96q/R74yH1sTW7ZGb2Xyycmacly4dYGd7+7EwrD4O+1q5crUZlOZd34eeYo8+lTrQ3On5mQWZJKvzKe1a2tAui4VtYiDFGDPOTeHtm5tae9e+ownrpauXLp9iXnn59GwUkPNA+oHBeLPY3DkFxKgnzlzhqZNmwLg6+tLWFiYZt3Zs2fx9PRk6tSpxMfH06NHD2xsbPD09CQ6WmqBzsrKQl//6YuWmVf6sPdF9kQkawL0tjUrcvy7QMJvZlDDwQorEwOiUrLoNOsw2QXSDciKE9fZ/WUzTA31+Xz1Of69lxY/eXsEywc2wMfR6rHKdSsjj0FLTnM+IR0LY33Gd65BV7/i1sesfN1yZ+cr6OnvhAzYcuEmjuVMGNbSQ6t1vMjp2FQmb4/QvJ697xq1nazxdymnCc6LrDsTz7z3dPvTVC73iCeQD3JxXXE6EMCK3vD5RWgXrLttgyFS61FRnw6Pt8FeejBCbqpua1HqvWkcVCrpqWZKBFRtDRXu3QDV7C7NA1zU6mViIy27dbk4OC9y/RilMjCG/jvg6GypwvUNkp6U3jhXHJwXKZnGmn0H/u5cvE3sIekBhUegNM3Rki7FIzQnnJRa3k3KSUF+bpoUsDccUnqZyqgD8Qc0wTlIUxrtiNmhuSm/X0RqBD+fKn6gsSJiBTVta9KxSkeCvINYHbEaFSrau7WnrWvbF17+suZV15fP4tR90zLeTM8j8W7uQ0dnL1CoNMF5kYP3dbUpkl1KnVhaPflc5WXoLrt0b+rAhFNSPdPp3sA4VVtpDxoHsGecFJyDdJO1/Vuo3hk6zYb1A6Rp2sq5ll43gnTDuXeC1Epeu7eU8t71D6g4B5LOg3sLqNu/9PcenCa9FwCZNGWbX5A04nzSxdLfk/yA5SUkZScRk6499/aRG8UjyI9uOBpXS1cu3r5IHfs69PWW0rN7V+vN9pjt5N/LMqhtVxtfOylDpmgU3iL/Rv3LmEZjNK8LlYWEXA/hVs4tApwDcLIobgFMyErQBOdFDiQc4NM6n5ZafplMRjWbagyuNZhFYYtQqBQ0dmisGRm4tBaQh43Q+zRk+vrYf/459p9rt4rp29nhumoVaStWoC4spFzvXhi6upJ18CA3Rn6HMjUVwypVcJw9CyM36YGCiZ8fRtW9yb98b07yewE4QMXvvsOgQgVyTp7CuJYPtoMHAyC3tMRt/Tpyz59Hbm6OUdWqWuWQm5tp+r2XZa9zffmy3F8vq9VwOi7tzQvQVSqpTr0bJ72OOyLVt72Xl759vYHSvWpRJo1jPSkTat2HxXX8zfPSmEmdZkn3bDEHpQetAaOljM4iGYnFwXmR+7s4lWZl7+LW8oSTUhemhh/D3kpSliaAoYVU95dB39b7lhH1RwCw8dpGxhwZo3no+qnfpwyqVTxjxbW71zTBeZGDCQe1ttkWvU2rf3dGQQb74/fTrWo3LAwtsED6zh6I185AyyzMJDQ5VDPWSWpeKjtidmAoN2TpO0sxN5S6HMw9N1fTEt/SqSU/N/sZfT19cu97KFL0ULeoH/utnFt0qNKBATUHIJPJmHRiEquvSNNqLr28lG/8v+F/Nf7HnHNzSp2OruSDg6f1QmqprKwszM2L+2PI5XIUCgX6+vqkpaVx4sQJNm7ciKmpKUFBQfj6+lKuXDmOHDlCu3btSE9PZ/nyB/yBlZCfn0/4vafBJVWxUnM2vpQ33GNnqNR5XzngRmwKN4C/zqRqgnOAxLu5/B1yFs/yRprgHCBfoWL61vOMCahIp2oWrA0rDgY7VTXROcaMoymcT5ACucw8BSPXX8BJLx0zQ6m1qWllGSHhUj9zAM/yRhhk3iA8/CY1zaBmI+kXnn4zljuJas4k5qBQQT1HUwzlMnZe0r2hCDl7DTulNSb6MnJLTKFmoqdEkZpAfUcTTiZIX1S5DD6tX67Uc/oolc6sRyuxVZFLwtE1ZDqVNgesLYat/8YyYT+FphVJd24FJY7palMdk9TiURpv279FSng4FU9NoVy01Eqj3jOehMZTyHKQnmLpB/6JdfRmkMm469YRRUIqeoV6eOibIi+RYnnbxJ2Ue8cyTI/BOnYLKn1T0ty7oDQpD24f3is/EB6OTCnDw9gG/bziC16KZQ1uF7VCxO3C4b4APu3YUpIKK2ORsA/HfO2b/fSji7FIPIie6t5Ttx0jSEgvJNP57Ued4jLjdNJpnWWh0aF4FHiUsjXsubVHZ9nBqwfxKPCgs0VnAv0CUalVWBpYcvXK1VL28GZ7lfXljYxCtlzJoECppm1VCzzKP97UT0XcLWVEJhe/tjGRk5EUx4WbsDsqk9i0AvwdTKnvqN3i7WRlQHx68UXZxUq/1HqnmYMe+68UJ/ZUtzNClp5IeHqizrbPi5eykIe1/yvDNhJZ9cHTMrrdCEfrMWdBFtcuHKPQ3BHeWY9B7i0KTStCmh6kaX9mvcJsPDZ3QV7UAhwylhvpBaS7dwT7DtI/gBIjemuoFHgenE5xXpaagj2TiDKug7HPlzhkfo/hfTdLAGq1iohH1PkKlQILfQsyFcV1nb2evdbv7C39t3ir4lsAmr9jPfT4ucbPHEs9hrWBNU3KNyEiQnqIbK5nTjbZmveby8219vdTxE+cz5BueGeGzuTHaj/iYS7VMQWqAkzlpuQoi+t2W5kt4eHh+Ov7s0G+QbPOzdQN63RrwjPCeUzJiqUAACAASURBVNv4bRr6NiRPmYetkS1x16Qb+/hk3RuGrPSsp7oWPrXu0oB1dwHCwmDECEiTrukFUVFE/zAafiiR/TVqFISEQFoaNG1KnLl58XW0eXNo3pxs4E5cnPZxjI1BodC65r5OXlZ9CQ++xyxrDsVmcSIhB2crQzpVs6SCXLf11UqRRnh4dinvfn3J8+/ieVf7+114/RTXHvg7M8Kg9RIs4/egNCpHuksb1BEReIVv1arz1RFbifD5Dur/jH7NWygNzDHKiKH8ws7oKfJI8+hGVuUmVDFzwDC7+FqUau1Dcng48vx07M/Pxjj1Mjl2vqTUGorKwAz97JtULZnKDuSdW0tMxS7ot1yAdfS/yJT53HXrQGFyPiSXve9eeHi4Jptnznnt4HTB+QU00m+EXCZdhXIUORjpGZGvKu4GVp7yWn9ThRm60z5np2QTrtD+7OaF2n38ZchQ3lYSnhVOakEqIy6NIL1QisEWnF3AlBpTSMpP4vew3zXv2Re/j9mHZtO+Ynta2rZk563ibgwBNgGcDTvL0HNDyVJK198ZoTPITs2mhW0L1kWu0zr+kotLqKdXjz3Ruve3AFZ5Vs9cd7yQAN3c3Jzs7OKKQKVSaZ5YWltb4+Pjo+n/4+/vT3h4ONu2bWPgwIH07t2biIgIhg8fzubNm0vdfxEjIyO8vb11lk/1VHFwyl5uZUpfCmsTfRRKNVkFSqpVtGB8z3o4WJtQoFBxPTUH1/KmWimZDolXIUw72K3i4oSLnTmgfSE3MTPH29ubYG9of+UWl25k8FaV8qVO65ayX7tfWb5CjamdE96Vpf4M3t7g7ZHKlgs3qWhlTN8Gzlga66YN5xUq6Tn/GBcSpC9jFTsz/hnWmI4W2Sw4fURr23b1PPH1tOPbDFMmbLmMSg1mhnLGdPHD282G/spyXFpzjpwCJT6OVnT+P3vnHd5kub/xT5I23XuXttABpWWPsnfZQ0AQQUFQHKCAgnvgHpzjcYtHBUQEWQoIyt57Q8sqpaV0QvdKZ9Ikvz+e5k3eJEX0eJbnd19XL8iTNzt53u+4v/fdpwNOjr8iymYPJ2y7TmExbSDK9vMRiAMElcRGciZsk6AbNXbK/fsswL+uAtabVRsVRgPhWRth8GPm++sqigGyyTKX74TSckUutBmH/5hF+KtdoTAFNjwkVVIDcnbAEyfti8B5/ygElsoyIf4uAoa+TYBJ+dOjBk7ID/eJ6oxPXBz4GkH+ceDl6gQGOSUmLHMDDJMLafwnwyXUhdW5q2lopMmqFComdZ5EnJ/9z9o51JklWUtkdNdhccOIa97Ud+POYDAayKzMJNg1GFfHP47y/K8OyP5d+2VptZb7PjhAWaMV5J7rVWyd14eYwDuv/C5qHo3+xwvsu1pIdIAb745vR7sWvjy59jybk8RYzs9XK3l7XFum9jDPiX7qFsy8tefJKa2lZaA7H93X2W53Jy4O4mNK2HbxFmE+rkzpHoG7052dtvQGIx/tvsam83kEejrxwvDWdI+6A9EdJ0+oLWnyapV/jN3zjoTCewSF0YSgdsQk2JndrasUyu915ULJ3acFpO0BndxxJLTqIqFxz8lva9CL2fjU7YJ2OfAlITRnVclXqxSNzzUOeo4THZqP5HRlhdLx9q+nEW97vM3LR16mSldFsGsw5ZQz59IcRkWNYl6neTgo7X8uccQxCNtC7QuuL/DcoefQGXQ4KB14oecLxLUQzyOlJIXkU+ZulNag5WjdUcYkmFk6b7i9wZvH30Sj1RDjHcPCAQsJ8wgjjjg2x25mZ+ZOPNWeDI8cbleh14T86nzCHcLBKo/19fW9o/fln4GG0lLSyuQxiGN+PjHWz6dr13/aczDU1lJ9/AQOQYG4tGlz22P/lXvmv2q/hKZjzP8krDyeybuN+kcAN6pVLJ+RQLnyKquOZ+GiVvHU4FaMtNh//1Q4Gif0PhrhGDNQfGbpeyHrmOiSx1oy8+Kgm6A6S6oi/i2h8LJ0hMKvpcXnHgeaAvh0jDR66Z5/Eh7cDg/8ANufF3T12OH4Dl+Er9oNvp8EN0Ty51xxHV9nBUxcBtrmsNvTzKoEnEPizI/VWTBDmiRH11WK2DWozb9E5d3lnItMt8NJ5US8xbiL4rICLCRYjAojsa1jZeOOr7m+xnsn30Oj09DWry0vD3gZtUpNpbaScI9wwqLDOKE5IY0p9Q7tzX097mNt6lp2Zu4k2C2YJzo+wYLoBWQfyObkrZOolWpmd5zNoHbivLLkwhIpOQe4WXeTPNc8lK62pfYqpyri4uL4S+xf6H29N5eKL5EQnMDwyOGcvHVSSs5NSG9I5/HWj+OU5CTTU/Fw8SAuLo72xe3Jysiyfhgmdp2IexOf0Z3ul/+UBL1z587s37+fkSNHkpSURKtWraTr2rRpw7Vr1ygtLcXT05Pk5GQmTZqEp6cnHh4iQPPz85NtwL8VxzNKpOQcoLy2gVUPdaO5vxvhviKQP3a9mLmrz1NSrSXY05kvp3WRxI2mdAtnzals8hu9yTuEezOwdSCOKiX9WwVIyucqpUI2Xz4gNpABseb5rSJNPRW1OmICxYc0qHUgZ7PMSXozbxcbRfiuLXzpaqFUnFtWwy8XbuHt4shdHUNxVTuw41K+lJwDXC+qZuPZXGb0juTd8e1YvD8dnd7Aw30j6d9KnKge7B1JYusg0go1dG3ui5erIzXaBmavOiuFdEk5Fdy/7CQ/zur12990e/TJ5HUiaDz2mZjl7vogRNvpqNdrBK3dq5Hu7xEMYz+3OsjOcLy9eSJrtBwCTybZrid9b6Y5gVCMv7YT2jba7tSUivt38xfz8zN32t4HiOu6z4ZTX4njW/SFro3006B46PccHPlI0Plb9BXUJWuUXrdd+w9GC68WfDn4S769/C16g56p8VOJ84vDaDRyvvA8OoOOrkFdUTWq70d6RfJen/f4e/LfqWmoYUrrKQxu/o8xBm5U3GDO3jlka7Jxd3Tn9V6vSzNN/234d+2Xu6/kS8k5CEbQhrN5PD+iCVsxO/B2VbPkAXmSUFGjs3HHWHUiS5agd4rw4eAzAymr0eLnbu7ab0m+yaJtKZTV6JjUNYyFo+PpHuV3Z4m1BZJzyll3OofVp7IBwYKaueIMx18chIedoqcMnsG2CbraXYgLuQXAiL/Ir7O2ZOv7NCgdGpPnViJ5tkaDVqj2mgLLIx/DI/shoJWYdbTc2wLtJAiHP4T9b4v/554S1MrZR6Hbw2K/NaGHRadfqRT+6da4k30UQWc00QDza/LJrxFiZMsvLcfP2Y/pbaZjMBrYnL6Z5KJkOgR0YGzMWJmFTnFtMc4qZ9zV7gxuPphdE3dxqfgS8X7xBLqaz50GbJ+T9Tz78BbDGRA2gKLaIhn9HYSo3fQ202W3zdHkEOQahLOD4Ddo9VqeOfgM+3P22329l4sv213/V8DB1xfndu2ou2g+r7oPGCD9v/7GDZRubjgGBtq59Z2h5uxZqk+exKVNG9z795ddp83KInPqVPRFosjmPWkSIW++8bsf64/Evzu+/FdCbzCy/OgNjqYX0ybUi9kDonGzKlCuPS1vGh1ILaJQU8+LI+J4cYR577hVUYuXiyOuavPtVxzL5LN9adQ3GHiwdyQLhrTivw6TvoOtC0QMGpMIw9+Do5/C7oXmY/o9Z9YeKrkOVzaL2LTtBDHeOPpDWDdNUNvdAsRlS6TvNusiAWAU7hnD3xMq8BV54OwpGjwGA6Ttkt/eJNCpdoURfxXPV1cD3s0h8TXb11RdLP4sLT6v7YQfHxLnIScvuPc7Me70T4S1qGa9vp6DOQfxc/GjrX9bpsVPk40vTm49GUelIxeKLlClrSIhJIEx0WMY0nwI5fXlBLsFs+ziMr5I+gKtQUunwE58Nugz1o9ez5mCMzipnOgY2JH1qetZdGqRdL9JhUlsu3sbS4cuJb86H1cHV8rry6nR1eDq6Gpf68RopFtoN5xVzjLLtn5h/ajWVbMxbSNFtUVMaDWBeD9RdIj0isRB4SATL472jsZR5cgj7R+RLNyUCiWPdRDNwae6PMX2zO1Sw8qEbTe2Man1pN/71gP/pAR9yJAhHD16lMmTJ2M0Gnn33XdZvnw5ERERJCYm8vTTT/Pwww8DMHz4cFq1asWTTz7JK6+8wurVq2loaOCtt976lUdpGov3p9usLT16g28f7CZdfmnjRUqqRSczv7KOVzdfYsucPgAEejqze0E/dl8pwFWtYlDrIBwbO+xfP9CFzedvklNWw7A2wbRtZn/+/INdqXxx4Dp6g5GO4d6seLAbj/WLol6nZ8flfCJ83XhhRCwqe2bpjUgr0DBu8VGJbv/9yWx+eqK3TDTJBNNc5u2s1SL8XGXiSutP59qkveezbq8e2ySsfsiAsP75ZrjoEAFc/QUe2gnh5s+B418Iy7OGWmjeByavsq+Q7uYPHe6DpFXiskIJvSy6zlVFIvi8A8sgABztdMpN3fPtz8PppaJL1f5eMcd5O5/2EYvEnKi2GvytaN6DXhYz91qNCOB/nGl7e/0/eab2n4DuId0lhWUQFNhZe2Zx8tZJQNhYLB+2XJoDGhk18g/1OP/w7Idka0TyVaWr4q0TbzEwfCBqla0S+H86/l37ZX5Fnc3aiYwSzmaV0qX577czc1ApcFQpqW8wnzRd1LasHKVSIUvOb1XUsmBdEg2NMz4rjmcRHejOAz1b/KbHF917Wyp3VX0DSTnl9G35K+rN9vaGx0+Ifcw/Viinm1BwBVaMhprGhN5kydZ3gfhrCtf3yro+aKvg7Lcw/F0Y9p7YE3XVoqDZc47t7a9adf8KLonAc8hbQuTo5nnhtW4qiNZXCTEkOx60NH0KklBcW9xkIgtw8tZJpreZzkdnP+Lby98CsCFtA9fLr/NMwjPUNtTyzMFnOJR7CLVSzaPtH+WxDo/h7+Iv2ZxZoo1fG7oHd+dkvthP1Eo198fdb3Ocs4OzlJwbjUZO5p8kT5NHn2Z9CHITasoZFRnM3TuXbE02nmpP3ur9FoMiBrH5+ubbvqYqKybDvxphn35C4fvvU5d6Dfc+vQmYPx99VTW5s2dTc/o0KJX4TptK0Isv/ub7Llu7lvzXzQm336OPErhgvnS5ZNk3UnIOUL5+Pb4zpuMU9e8X8fx3x5f/Sry/M5UvD4oC/v7UIlILNDYFUW9XecyjdlDK9tuyai2PrjzD6cwy3NQqXhoVx/3dm3Mxt4LXtpiLUJ/uTaNNqCfD2shVuf/jEdBKJMmWOG5le3XiC1EovXkelo80x6vnV8GD24T15IIrQu/IKxwyDkDSauFE5OIjEmlreDeH2nJYN1VoDzm4wKBXoNcc8IsRonUm+DcWPoxGsSfPvyJm2APjbG2ED74PBxcJIc+QjjB1g4h/tz1jVqCvr4AdL8LjTWgq/RMxZ584H42OGs17fd8j2jtaEokbFD6Ip/Y/xd5sQftu4dmCFSNW4OvsS7BDMLmaXD4594lEiz9feJ5vL3/Lk52flMWTptubcKv6FpdLLtMhoAMNhgYe2P4A1yuu4+7ozsIeCxkbM5bVV1dT2jiKGuERQWJEIq6Ornwx+Au+TP4SjVbDxFYTGRg+kClbp3C5RHz3V15eyZKhS+ga3JVA10Be6PYCH579kJqGGrqHdOehdqLh9nC7h+kR0oMrJVdICE6QbN8CXQNRoaIBeRx/qeQSk/gPTNCVSiVvvvmmbC06Olr6/6hRoxg1Sq6UFxQUxJIlS/6Qxzd5hlsis9hc/dIbjGRZHXOjSF5R9XB25O7OtvZRTg4qJiXc3q4ivVDDZ/vMRYKknHKWHclgwdBY6c8SOaU1vLjxIueyy0ho4cuiCe0I8XJh1Yks2Sz8xbwKjqYXM7JtMB/tvkZpY4HB3cmBsR2bUK28DRxVtpHZ73ZZc3QDGzVDgzk5B9GpubTBnKCX58Cul80dnKwjovuT+Cp2cddn0GqooBO1HAqhHaHypqh85p0Rlc8xn0Jri0Qw/5KobHpbFS26Pig258pccTmiJ0QnwvV9cPJL83EX1oqqbHurH1rBFSFC16KvUHO2tmCyhJuf2c/S2dP2+jstKvwH42DOQSk5ByEMt/n6ZrtB9W9Bvb6er5K/EicA/zbM7jAbLycvMisyZcdV1FdQVlcmBeX/Tfh37ZdedmzNzueUM+Hvx5mcEM6iCWYl7eKqep7/8QKH04uJD/HkvbvbERdi57sMuDk5MKt/NJ80qgg7qhTMSxSCVLsu57PyRBbOjipm9Y+mS3NzMS45p1xKzk04l1V22wTd2lLyQm653eTcBMuiQZPQ2umu1VVAcDvb9UPvm5NzsG/JVlUkLNYCWkNwW7Fmjw5uCtR6zBKK8NpqcG+iQ+oTKRckUruLjpBCAfF3iT8T9rwhAlZDg1B/VzqYVdxBdGN+BSW3ofwD0njLD9d+kK3/cO0Hnkl4hrVX13IoVyjFaw1aPk/6nMSIRGJ8REGzrE4Uhn2czd+HLwZ/wfYb2ymsKWRI8yGSCvuVkiv8cO0H1Eo1U1pPkdZfPfaqpCTv4uDC0qFLaR/Qng/OfCAV8yq1lbxx/A36hvUlq8KWlmiJO/FB/y3Q3brFrYWvUnvuHC4dOxLy1ps4Nmv6vO0YEkKzD+WdvJKlS0VyDmAwULriOzxHj8alnZ3v5m1QskzuRV/63XcEzJ2DwlGci+zZvOnL/1jRvN+Lf3d8+a/E5iS51saelAKq6xtkXfT5g1uRlH1KihXnDoyRjUZ+vj+d05ni86zW6nl9y2WGxgdzLtv2Mz6XVfafnaDrdcKRIqQjeNkMR5phvb+a9tZTS+TNpOxjQvgzvJuIw/xawooxIhYFEVM+sk8UOztNEzEjRhH3dZ4mmEyZh8WxDbWiax9/F9z1KfwwQwiCeobBqA9ELPrDdChJF8n9xOW2yXlZZuN4VON58FYSHP0YBr8pYl1LlN9GaKtBK6w0HZyhzTjbx/kD8EvGL0yLn0av0F70ChWs29P5p2XJdWZlJuuurpPs0LIrs20E1bIqbfdha0aUg8KBUDfxeX967lNJINTUnNl7z1423LWBrRlbUavUjIoaJY09JgQnkBCcIN1XUmGSlJwDNBgbWH9tPV2DReFrUuwkRkePprahVqbEnlSYxKb0Tbg7utOnWR9p/fjN42gNtkrunQI7Nfne3Sn+lFKWkf5u5JXLu0MtgyxERZQKBsUGypTNh8T/9sDeYDCy60oBaQUaBsQGSmrudgsEJU1bJixYnyRtoAevFfH8hot891A3ma2KCUqF6DptfqI3a05l02AwMjkhXKLu3w555bVsTsrDTe3AuE7NiPS37RTZydnvDK6+UGe14Qe1FZVIS3habKolabb0yqJG8SOjUXSx03aJKmOf+aKSGT9Wfvye10VyDkJRc/PjEH1VeBOvvLvxOoVQ7xz1N/PtPILFzPm1HSK4jRksuuRFdsSXiq7KL1sqJTs4w33r5FSj4jQ4sEhsqO0miMc2wZ71gmPT85H/bBiMBpKLknFxcKG1751Tm61RWl9qu2YhrJdWloazyplwz9/mxfr+6fcl5cykoiRyNDksTlzMoIhBfHPJHGS28WvzX5mc/zuR2DqQd365gs5OVW7t6RxmD4iW1Nhf33JZ2i+TcsqZu+Y8exYIWmx+RR2HrhXRwt+NbpGi8z5/SCsS4wK5mq+hV7QfYT6unM0q5bFVZzE2PtzhtCIOPDOQYC9BOe4Y7oODUiFL0ru0aLqTv/J4Jh/uvkaNVs/93Zvzyqg4qWjZFEK87sClot6OirupzXzzvAgSwxJEMlxTLD/MaBDuDKYEPfMofD/RbJnT/wUY+CJEDYTQznCz0QPbxcc8GgOCzWNi9Bj0gpJpcq8I6yqKmPkXRMfH0VXQ7u3Nu2UehSMWSZ6JgSR7abe3xAOI9Y3F1cFVNoPnoHBAb9QzMHwgM9sKZpCH2oNqCyqoScnW0j/dhOsV14nyjuKtE2+xMW0jAONixvFaz9dQKpSoVWrGxsj3+4zyDB7Y/oCkCr/1xla2jNtCbUOtzOattqGW5ZeW89HAj2zU50vrSqmor2BA+ABWXFnR5Gu+p9U9v/q+/BbcfOklao4LwZLqY8e4+eJLNP9OPH7txUuUfP0V+koNPvdOwnOkfbaRNts2KNdmZUsJes3p05SuXo3C0RG/GTNwbsoezTq2sLrsdfd4NLvN1nzq6GhcOnS4o9f5//jjEOTpzC0LppOvqxonByWVdTr2phTg5eJI/1aBHHl+ECcySogJdMfLxZHLNyuID/FEoVCQVihnguj0RjJLqmXFURPsrf3HIH0vrJ5kLi52fUhY+dpD3wWCRm5CnwXiO24nppbtf5mHzMk5iJjy9FIY8qYYu+z3rLBEM1HPi63iRaNBxH8xiTD/shid9G4uEuSlQ0RyDkJ1fssc2w54WSY245ylN8R4Uvw4uGQhVGYax7RGVRF83BYaGr832xbA02ly5tcfhPI6edHOMuazt9YxsCPeTt4yh4yB4cKiUqPV4KRyQq0SDKtzhedIK0vDUenIU52fIsBVMN9uVMr38ypdFcW1xUR4RkhjTUmFSaxOWY1CoWBq3FTaBZgLmKYRJ0s4q8Ta9ynfS3bAk2In8UzXZ1AoFFwqvsSDOx6UqO/bb2znl/G/YDAamH9gvl0V91tVt27zzt0Z/pQJ+sLR8Qz7+LBszXIWB+CDSR34685UkrLL6R7lyzNDYzEYjGw6n0dybjndIn0Z3f42FTrg5Z8usaZxxvHDPdf4fEpnRrUPoVukH96ujpRbzHeOaGu/Kmk0GqXk3IRTN0S3YmqP5mw4mytR2juEe9MzWnRiw31deW64bUJVp9Pj5KC0Se6zSqoZ/dkRyYJuxfFM5gy0Vd1W/x6BOBDejjZr+dB2onlTCekAXWaYrw/vLgLTWovX36pRzOPwB+YkOG2X8PW1pjEBFFjNCdaWCerQ5U3mxB0jnF4CHadAMwtrOSd3aDdRfvvoRNsOU8uh5v/XV4kE3YSGOpGMRw1ovKyF78aa/dSzj4HKSVRbwf4mae+k8S+ARqth5s6ZpJQKqu2Q5kP4oP8HdgtDv4ZB4YP4+OzHVGpFcqNWqhkZOZLahloe3/M4ZwrEZzEmagzv9Hnnjh9jd5bcv/lw7mHqGuqY03EODkoHDuceJsY7pkl7pf9H0wj3deWx/tEsO3KDuga9lDibYGlXecZqj0ovrKKsWsv1oiqmLjtJnU4U2h7o2Zw3x4oucfswb9qHmWeed1zKlz1Gnc7A/tRCpnQT7JZgL2c+ntyRRduvUlatZVJCOPd1sz+uczW/koWbzb/9b47eoF2YJyPbhRDm40Jume3ITadwb9qE3oElppMX1mKgGPSwaqKYRQRBI39gM3ScKmx4TAjtJLQnTDi4SO5ne+Qj6Pk4OHsJkaGULaI7Hz+26W75lnnmxPrgX4XYUNsJMOesSNq9mon7s4fCK7Zrrn7yrn9Ygu0xVtDqtWgb5MWPVt6tWD5iuUygcV6nebxy9BUMRgNKhVL6XfYL6yezVXNxcCEhOIEDOQdk6rgb0zbSt1nfJjUqtt7YKiXnIJgz+7L30Tmws82xtXrxHRgQPoCVV1ZK6+392+Pv4o+/iz/v93+fNSlrqGuo40qp/L06W3CWjoEdf/W9yX36aTQ7doLRiHtiIuGffWr3uJozZ60uiz2xoayM7BkzMDTORdecPInS0wv3Pr1t7sNjyBDK16+XLivd3HDrLbpXdVevkvXgQ0KhHajas5eo7dttfM0B/B55mPyFZqaa34MzpO45gMfAgYQvW0rlL1txCAzE94FpKFR/fBfu/3F7vDQyjpkrTqOpa0CtUvLK6DjyK+sYt/gYxVXid9A7xo9VM7szol0IH++5xmf70tEbjLQO9uC7md0YFBsgs7H0d3eiXTMvnB1VvDWuLZ/tTUOrN/Bgr0iG/id3zzfMlMdlZ76BgS8L+rc1EmaKLnv2MWjWFZr3FOvdHxOxoWlPbtFXFDxNsOtHbrHmY0V1bzUcUizGjZy9RVwLIhZVu5u719axamGKmFdXWhQIwruDW6CYhTchrpENdden4vFzT0NEL9G0soeNj5qTcxA0/F0vN23r+TsR5h5GQnAC1bpqcjW5RHtH06dZH/xd/CmuFYVrlUIls991dXRlydAlLE5aTEltCWOixzC4+WDm75/P3uy9uDm6Ma/zPKa0nsKGMRvIqMjAz9kPb2dzHDEgfABXS81Ns2ivaFnH/UbFDWbunCl1tfdl72PT2E2EeQhGdGvf1gwKH8S+nH0AeDh6MC1+GqmlqbK59++ufEe8Xzyjokax5foW2Vx6UW0Rh/MO4+vsKytIW+JW9f8n6HZxMsO2inMio4SoAHOHwdtVzbvj5bSwN36+zPKjmQB8dzyL64XVkl96RY2OC3nlxId44ufuRHmNlvVnzEGc0QhfH85gVPsQ3J0cWPNIDz7bl0ZxlZaJXcIY0S6EQk0dz/14gWPpJcSHevLXie1pFeRBh3BvknPMFSWTWF1MoDu7FvRj64VbeLuqGd0+pMmZ9YpaHQvWJbEvtZAgD2feGtdWxgpYdzpHFnBnFFXToLet+gR6/DabJQkN9bZrlXkwcwf0e0YktmFd5cmo2g2mbYJ970BVPrSfbE5kL8qpkmQeFkqaHlZd0phEMX9pgm+U+CvPtn0+5dkiQdfVicqnX4ytantAK7j3e7OwW4/HxXyS5etssJrdtfRtzztrTs5NSNlifl23zts+r5rb00dvB41Ww8lbJwnzCPvNHfAfr/0oJecgkuETt07QM7SntJZWlsbCowu5UnKFbsHdeLvP2wS72Z7E/Vz8WDlyJWtS1qAz6Lin1T1Ee0ezOmW1lJwD/JzxM2Oix8ge43YI8wiTVWADXQNxUjmhUCiY22kuczv996jf/6chvVDD53b0OgA6hHnJ9DW6NPdh60XzCSfK3w1vV0cW70+XknMQYnBzBsYQ6GlbpY6ww/KxotQViAAAIABJREFUXhvdPtSmMFqoqeOHM7nU6/RM7BJOhJ+rbL80ISm7nPGdwvhxVi+WH7vB7ssFZFiMNp3PKaeiRmuX2i+HHRp8wUVzcg5CmC15jQgE1a6iw+3TQi7KBvK9AQSzR1crEmpHZ/noTE2p6HYXXROqw10eFGvJqy3uwCjo6m0niMDOshiQexZ2vSK6Nm3GC/GhqAGgUIn5cxO0VkFoqf3vgCWyKrNsZuzSK9Jt3BPGRI+hY0BHkouT6eDfQWLMDG0xlJfrXmZj2kY81Z7M7jgbX2dfMioysIZp7WrpVb67/B06g47JrSfTJagLPk62HT4fJx+ivKNkM+sKFEyOFT7CT3V+CkelI0fzjtLSpyVPdTb7kA9vMZzhLYbz3MHnbBL0n9J/YmY7O5ohFihbvx7N1m3S5arduyldsQLf6dNtjnXp0J5aiyTdpb2g0FcfOyYl5yZodu2ym6C79+1D6Ad/o3z9Dyg93PF/7DEcfMR7Url9h5ScAxhqaqjavw+fyeJ90JeXg1KJytMTn3vuwbllS6pPnca5TTzuve08Vu/euHbpQu6TT5HWrz8qLy9CFr2HR79+t31P/ldQWq1l39VCgjyd6B3tL43ZgNhbn99wkYu5FfSI9uP9ie0JsrMn/hq6Rfpy/MVELuSW0yrIA393J97dliIl5wBH00s4kVFKmI8Ln+xNk4qgV/M1fHUwg5dHxlGt1fNz8k1CvJx5dlhrnBsbMdN6NGfaHaq7F1fVU1xVT2yQx+8q4v/DqLWl5LNmKpRniLhu5PtmoeHidDGu6OYvGkMmhHQQeiIpW8RIUOsxYo91bWRqRQ9qnB9v3BMdnKHzA5B9En5+UsSNrYYLXSJXX+g0VezxyWvAPVjMuTu6wk+Pixl2gI73wV2fi1g1xexERPRAeXIOgk05/WdR2NUUQId7xR+IWNXEBLCOW/POwom/iw5+3hlscOOg7drvxNjosfi6+DI1bip7c/by2tHXqGmoIcQthMWJi1k5YiXfp3yPRquRvMyf3Pck2ZpsBkUMYnaH2Xw2yCxkuvTiUvZk7wFEN3zRqUX0bdaXUPdQtlzfwub0zfg4+zC/y3z6hfXjsfZCnO1AzgEiPSOZ13me7Pu4J2uPjHJep69jb/ZeScQ0V5PLon6LOJN/hqLaIvqH9cfPxc9mPAvgYvFFRkWNavK808qnlY0InQl+zr9N2NYe/pQJ+vZL+TZrH+y6xn3dm96I9AYjq0/Kk7qVJ7J4cnBLDqQWMnvVOWp1etQOSj6+tyO9om3ffMstKy7Eky/u7yK7/rXNlzmQKiqZSTnlzF19np3z+/HBPe1ZsD6ZC7kVdI7w5i8Ws58hXi483NcszKJtMPDj2VyuFWjoHxvAwEbV+E/2pEkU1PzKOuavS+LES4mSJZHSzoZaVmNLB/V1+700GL3tUn1jcGpPhdiE0E4w9UfbdY8QObVc7QFOdhTQB7wkutbXGhWTh74jNrA24+CcBX3R2UvQSrNPwtr7BDXVyUt0o1paWSDFDrey5bCAmx/EjZZXTS1ZAd4RtgGxr4Wwzk2LuVETbGb37wyppalM3TZV2hzujrmbN3rfudJuUW2RzZqp8mnCC4dfkOwvTuaf5O0Tb/N5orXCvkCUVxQv93hZtnazynYe2HJt5ZWVLL24FIPRwPQ203m43cOyY59PeJ65++ZSWleKu6M7r/R45XcFB4U1hfyU/hNGo5GxMWPtFhn+1/CX7Vdt1tydHHh8YLRMcR3gtbvi0dQ3cCStiLgQT/4yoT0KhYIarfx3bzAiJezHr5dwOK2I1iGejG4Xwj1dw9l1pYDDacUoFDCpS7jdfdQSFbU67vrsqOSosfxYJtvm9aVrC1+UCrlmRrdIcV/BXs68OCKOVcdtZ9tWn8pm9gBb5pAMxdds13JO265VNRrAx40Rf/bQZQb8YtHpaDVCjNfYw9r7ILuR8pi2U7hbdJqGjYqbwk4nU1cn6J8myv2xTwU7qe8CoXJ8+G+iuNjlQdj+rPy2xb+eoHs72VF/b0R2ZTZqlVr6TYV7htsdZZncejKTW0+WrfVt1pfPzn8mqfAqFUr6NOtDYU0h07dPlyj1e7L3sHbUWsbFjGNT+iZpT+oW3I1+4SJh/CzxM35K/4k8TR6Dmw+Wut9qlZr5XeYzv0sTHSdAb7Q9fxmtKSV2UGmRnEtr23fgO306DUVFGOrrUYeJpCH07be5+fwL1CYn49y+PSHvviueX4QtS0Td3D5zBMBr1Ci8rGasARzsdModgoIwGgzkv/Ya5Rs3gUKBz+TJBL38Ei4dO+LSUc4QMOr1VJ84AQYjbj17kPfUfKoPiuBeX1pK7mOziD17BqXrH2dr+d+ItAINE/5+jMrGpseItsH8fao53pu3JokrtwSb7NC1Il7edIml00Wntlar50RGCc18XGhlx1oSxHfvelE1gZ5OeDo70iva3CWu0dqKytbqGsgtq7VhQeWU1qBUKnhiYAxP2GFM3im+PHidD3alotMbiQ0Snfk7LTgYjUaOZ5RQrzPQO8YftcOvj9TcMXIb98vUbSJRfnCbYFsuH2FupJxfBQ/vMTeHfJoLgeHMI7A4QRQ0QzrAPSvANxJm7oZz3wn9pPaTRcL+cTvQ3DQ/1p7XhCYSQM8nxJ8JV7YIlyATkr4XSf2YT0RinXVUdPWHm7u1XNoAu18XRYjO02DCMvncuNEI25+DM8vF6+j2KAxrtPIsy4Tlo+wLNZvg1/K3vrNN4s1eb6JUKtHpdbxz4h1pj75VfYsPzn7Al4O/5PluzwNihHL0ptHkaEQzM708HUelI7M6zJLu71qp/HxrMBpIK0vj2M1j0hhjSV0J8/fPZ9fEXfi5+DGr/SwGRwwmxD0ET7VcC8fSDcSEINcgMsozmLNvDjmaHLycvHin9zvc3dI8JtAlsAsKFDK6etcg81z6Lxm/SFomA8IGkBCcgEKhYFG/RTy1/ymscbXMNr76rfhTJujdIn04niHvSpZUa8ksrqaFvxv7Uws5kibEjsZ1aoZKqUCpAFe1SiYi5OYkfiDvbE2hVidO4NoGA2/9coXjLyYyOSGc7xuTeqUCHut3e4XTM1YK6akFGirrdMQEerBlTh/0BuNtVd0BnvkhmS2NFkbfHsvk3fHtuK97BBfz5B2lqvoGbhRVS3PxU7pHsOZUtqRcHxfiyb0J4Xy+P13WWe96m5nP34zCKyKwDW+kUBanweWfBJWz/aTbz14Pfg1WJkNtqaCcD3lDdKoMBshPFtVKzxDRhRqxSPxZInqQCEzPrhDq7n3mi3+3P2cOYusr4JcFMN+ORZwl9DpRofQKF3TSu5cKelXhFZHcW87FezUT86H73hYd+KC2ouqpqxVidiZRuj8Arxx5RVa525i+kVkdZnEo9xCb0jfh7ezN4x0eb1LsaGTkSFanrJaCU0+1J32b9ZWur9HVSIGwCclFydJ1i5MWc7bgLG392zK301y87IhNDWkxhJUpK6UA3MXBhX5hIqA+X3heZtHxyblPiPeLlwRHQAg17Zm4h/TydJp7Nrfp2FXrqlEr1TjaEdqr1FaSXpZOkGsQ92+7n5I6sSd8n/I9G8dulAmA/C/CerQGoFbbwOONCazRaKSiVoe3q5pAD2e+e6ibzfHTejbn5A0zw6F/qwAi/FxZfzqH5zZckNaPXy/hvbvbsXJmdzKKqnByVNHM2/z7zyuv5dujN6isbWBSQpikIr/zcr6UnIOg3W88l8eTg1vywaQOfLQ7jRptA1N7NGdUe7lQo3XxACCn5A7slQy2ATBqV1Ek1GrEZZVadKmtockXM4PNOoODk5iTdA8SWhf+saLjDqJrU3wNgtuL+y7PMSfnJlz8QbhDdH1IjOiASM772AYD5F+0nYfP2C8S9LjR4g+ELoZ1gm7PwtIK9hJYT7Uns/bM4mjeURQouLvl3bzW87XfVECL9Y3lwwEf8u2lbzFiZEabGcT7xbPu6jrZvHuDoYGdmTuZ13ke60av41T+KdRKNV2CukiP5+LgwpTWU+74sS1xT6t72JUlt0aaGDuxiaPNMNTZdk4MdbXkv/U2ZWvWgMGAW7++hH36KeoWLWixbi1GvV5GF3dp1w6fB6ZRtup7MBhwTUjA+97JNJSWUvTRx9SlpODWsyf+c55A6dQ0w8173Dgqf9lK7Tmha+CemIh7v35odu2m/AdzEbxs1Src+/XF3aoTbqivJ+uBB6hLFr9bp7g4tOlWxRujkfKffsL3vvt+9b35M2PJ4QwpOQfRFLpys5L4UE9qtXopOTfhbJbYI9MLq5j89XGKq0Qc9kjfSF4eJdcJuFVRy4xvTpNaoMHZUckro+JlBdPJCRGsP5OLtjFWjfR3o09MAEaMBHs6y/bLke1uI157h8ivqOP9nanoG6uhqQUaFu9Pl0aZbocGvYFpy05J8XikvxubHu+F96+ymOxBiV12kwlZR0Wz5vQyOcsx74zYW5tb2AcbDLBplkjOQQhu7ngR7lsrOuOWe2zpDXNyboKpYHvxRzjwnhD0THhYsEUt1dtNKL4mxOPGf2l7XUWuoKWbzjsnvhBFgQQL9s7VrXDqa/Pl45+LDnzMYEj55fbJOYCTfUHX34M3jr7B/IT56I162Sw5QE6lfDQsozxDSs5NOJR7SJag9wztyfbM7dJlFwcXOgd15u0Tb8tupzVouVB0gSjvKGbtnkVuVS7OKmde6v4S41uaz8UjIkfw8/WfJTZV79DeJDZPZO7eudJzqaiv4PXjr7O72W4cGgUFo7yjeLvP23yR9AW1DbUyO2C1Ss2PY37kbOFZ3B3dpeKvzqAj0MX+aJriTuxRfgV/ygR9/pBYlh/NlG2gIMThVhzLlFlLnMkq5b27RTdowZBW0lyjUoHkCVlQKT8JF1fVYzAYeXtcWwbGBnKtUEP/VgG0CfVCpzew8VwuaQVVDGwdSO8YcxLQOcKbnZcLpMstA91lapuWyfm57DI+3pNGWbWWe7qG8UDPFpRVa/n5gnyj+O54Jvd1j6BnlJ8s4PZzU9Mq2Ezpb+btwq75/dh28RauagdGtgvBRa3isX5RfLwnjQaDkRAvZx7uE3nH7/MdYcfzQgUz96yoaprmB5NWw0M7zFVNTb6gibr4iJmb0E5CZCPvLPi3FF2nypvw3ThBM1KooP9zMOCFph87fqytqFxZpvxyZa5IwEtviMC1MAVihoiE38lDWBetuEscp1AJG42+C8QcaVPo85SgPlUXCeVmhUKoh6bvbvo2vwP26KHLLy9nzdU10uXkwmR2TtxpU2UEaOvfliVDl/DDtR9wdXDlgfgHZLM+ro6uxPnGyWjwJmXKd0++K82UXi65TEF1AZ8lfoY1OgR04PNBn7MudR1OKicebPugJPZxvtCW7p9UmCRL0AEcVY6SSrQJdQ11vHzkZfZk78HN0Y2nOj/FpFgzXXh/9n6eP/w8tQ21OCmdqDeYKYFl9WVszdgq80n+X4Sjg+0JxLRyMbeCJ9eeJ6O4mtggDxbf34mYQNtuz+j2ofi5ObHrSj6R/m5M6io6p8uOyIVcfjiTw0sjW+Ph7CgbNQLREZrwxTEpsNxwLpcfZvWkU4SPRMW0hLOj6MCM7xTG+E62Thsm2Es7K+vsMH3uBAGx8NB2OPGlYLx0e8SWGXTyK9j5kgi03INg2k+Cgt56lPgz4dIGQYFsqBP73ZS1ENRGUCMt59U9GxW+R74vOjBFKSIgs8dI8o8RNj+WgVqwncKc2o6Q3B0g2C2YCI8IqYMAwjP2aN5RAIwY2ZC2geGRw+kR0oN6fT27Mneh0WoY2mLobYthiRGJJEYkytaa6oIAOCgdbPaI34KLRRfZnbWbUPdQxsaMxcXBhbRy26C6ucevU38dAwOxTtEVTs6UfW/uoFUfOkz5hg343i8cLezNcge/9BJ+Mx/GUF2NU5Q4B2dNf4KakyLIrLt0CUNNDcELX5Fuo9m7l5JvlgNihtxj8GBarP6e2ouXUKjVOMeK+KU+zfa11ael2yTomp07peQcoD4lBRxsQ0Sl5x3oOPzJYU/jwtTZdlGriAvxJMUiSe8cISiyX+xPl5JzgKVHbjCjd6SsWPnx7jRSC0QhsE5n4M1frjCqXQg+jezGts282DKnN5vO5eHp4sh93SIa2UQKVj/Snc/2pVNQWce4js0Y18m+S4BOb2DX5QLyK+sY1iaIMB9R+C6orGPR9quk3KqkT4w/zwyLJa+8RkrOTbAnhGwPe1IKZM2yG8XVfHP0BguGxN7mVk3AyU2wippCQJzQ+LGnWG6t6l5Xbk7OTchvbNTUlouue02JaCT5txJK7JbNlYgegnm08RGz0PG+t0Ss2nKoaNCY1hVKaDVM/D/jgBA2rioSmkgDXhIxrnVROOekPEG3HOOUnu8lcT64nYOQCdqqXz/mDrExYyMF9QV8OfhLOgZ0JKkoSbousbl8Hw9xD7ERF43yEo3M8rpySutKGRczjuLaYn5K/wkfZx/mdZqHl5MXbf3bsiNzh3Q7lUJFnF8cfz39V3KrxGdRp69j0alFDGsxTGreKFCwePBirpdfR6lQSqOfmZWZsudWXFuMRqvBx9mHi0UXOZB7gBaeLfh5/M84KkVeVlZXxrOHnuXkrZP4OfuxsMdCScE9oyKDWbtnNTlrrjf8zljDAn/KBB1gaHwwP54z/6DGdgwl3NeVFccyZcf9cCaXV0bF4+bkwLSeLegW6UdyTjkJkb6Syvn4Ts1YYUGXHNMhVJo3GhwfxGCLWe/565L45YL4wJYeucHf7unAxC4iiHxrbFtqtHqOphfTJtSL9++x39msqNUxfdkpSRzuYl4FPq5qBrYOxFGllCqnILr+AE8MiqGsRsf2S7cI83Fl4eh4nBzkG5WfuxPTLCyLarQNfHHguqSafKuijq8OZbBwdBPKr78HpY1J5Okl5uQcIOeE2d6iOB2WDjLPa4Z9LfzS1a7C3sKEwx+aVTONejj4FzHfY7JQu5kkxOXqyqHzdFsBOBAJuyX1PXak2LzX3W+mtiatEpYbYz4Wj2HamI162P+uoJ26W3gpX90mZtaNBpG4t50gZp8sxUtKbFWM/1HY61RZqyVX6ao4nX+aQeGD+Pbyt/yc8TMBLgHM6zSPNv5tbCworLGo3yJeO/qamEEP6cbCHgsBJIENEw7mHqTB0CBVI29U3MDd0Z0A1wD6hvWlb1hfm/vuGGArwGRaO5N/hm03thHgGsCU2CmywgHAqpRVUsdLo9Xwzsl36N2sN83cRUDy3qn3qG1MViyTcxMcFH/are+O0SvKn83J8oJfdKDY8579MVma304t0PDSxkusnyV0A26W13Iht5yO4T4EeznTM9pPEq80wTr5VyoV0piN0WhEpzdKVMdD14pkXZ8Gg5GN5/LoFOHD0Pgg4kM8pY5UM28XaT+1h5vltaw8kUVNfQMKbJP0MJ87cUyw06lxDxY2a+MW270FdZWw+zVzoFVVIIK0Kavlxxn0sP15c4entkzMjT+8R/in73hR7DOu/kIACUSBr+Vg8QegbxB2PhfWiec19C0x3zj2c3HfNcVC7LKfdacceQHgN0Bv0FNYUyhbs+6MmNYSghKYsX0Gl0pEULk4aTFrRq0hwlPs00mFSXxz6RvqGuqY3HoygyIG2dxPv7B+DAgfwIGcA4DYFyzFhky4Xn6d1NJUycMWxPdr2aVlbM3YSrBbMPM6zZMKfIdzDzNn3xyJ0bMnaw9Lhy2loLrA5r5Ty1IZGDHwtu+L18QJMrVzAKfWralLlo8yabPk4xaaffspWbIEo16P7/QH8Bo1SibmptdopORcus2ePVKCXnf1Krlz54lOIJB7/jyRGzfg3Lo1Lu3knU33fn0p/uILJP6zUolbnz5YQ19eYbOmcHfHaGWv5prQ1ea4/zVU1Ops1g5dK8TP3YlIfzc+mdyRZ3+8wMXccnpG+/H2ePGZlFi5TBiNwqfcMkG/YcXy0TYYyCuvlRJ0gNbBnrw4UhTdvz50nc/2plPfYGBKt3A+uKeDFJ/qDUZOZJSgUECPSD9p/ZHvzkjjln/bmcq6x3rQPsybx78/x9lGpufVfA31DQZeHRNPM28X8srNRYmmRI/rdHpWnciSGlSH0optjjmYWvT7EnStnb3Lr6XoWPvFwPi/i7Xuj8GljWa2U4u+ZmtfE1x9RQEz31yQInqg2FuXjzCLax5fDDN3Cjbm1vnC5afVMLFXp2yxdSHKPCJizHu/F5bBIOj0we0Ea2rNFPMefOh9McrZapjtWKT1840aKDr1EhTi+YJoaMUMhnQxxy1jepnwB3bQAY7lHaNeX89HAz/i8/Ofk1aeRp/QPkyNn8pfTv2FYzePEesTy4KuC3i156u8c+IdNDoNcb5xzO00lxWXV/DJuU/QGXTE+sTy5ZAveaT9I4BgS9XoapjSegrXyq6xLWMbnk6ePN31aYLdhJe6JWoaaiipLRECdBeWsOTiEhoMDUxoOYEXu78oHdc/rD+rr5rPxx0COuDj7MPerL0yJfYDOQf4YMAHgDh3meyDS+pKePnoy/QI7YGboxufn//8tkJw1mzP34M/ZZR68FqRLDkHMe8I2HRkHFVKWec6NtiD2GB5p+iV0fE083Hh1I1SOoZ7y2bCLVFcVS8TUgLR4TYFlIGezqyc2d3mdsk55VTW6egR5YejSsnJjBIpOTdhb0oBYzqEMqtfFJ82eqxb+gs7OQhFzrfG2dKO6nR6skpqiApww1Flnv85mlZkQwP95cLN35egW28wJpgUKO1Z+ZhmKc8sk4sp5Z4WysjRVsGRdcXTaICKPJGg15aJTrdp7v3GIaGoaQpqTRjxF6FinHVUCIsMeAE0t2znTk2iGhVWgm8GnQi+TQl6wRVYN9X82n+cKWw1wqyCmNYj4eTfbd+DfwANelsqrr3Z6kivSDambeTDs8JuKa0sjUvFl9g9cbfNJpJfnY+Psw9OKkGljPKKYuXIlTb32dyjuRSAAzRzb4aD0oEqbRVz9s3hbMFZlAolU+Om8myCnUQB6BzUmWe6PsOyi8vQG/XMaDODXs16cezmMWbvmS0F0bsyd/HjmB9RWVTGU0vl9iYGo4Frpddo5t7MbjJhDctO4P8qesf42SToiXHBNOgNXM2Xn+BNCfLmpDyeXp9Mg8GIo0rBJ5M72aVRPjEghidWn5NmxGf2icTNyYF9VwtY+NNlblbUktg6iA/v7WCX7ujjKirYzo4qNj3Riz1XCtHq9QyJD8bdyQGDwciFvAp8XdVE+InvsKZOx7jFRynU2BGsbIS/xx1QKxUK28x+z6sQ2ccszqPXCdaPV5g4vrbUlmZob5yloQ6qrYLWisbjuj8m9svS62JvamoE6OSXggYJosuzbhosuCIKkvHjQFctV3XX1sDeN4Rokv/vCIyBzIpMGyGc/Jp82cyes8qZvs36cvzWcdneUKmtZH3qep5JeIaC6gIe2fWIdF8nbp1gxYgVNp6xKqWKzwZ9RmppKjqDjrb+tue1FZdX8LczwlHDUenIJwM/oW9YX9ZcXcMn5z4BxMzjlZIr7Jq4CyeVE2tT10r7CghdjfSydLR2dEDOFZz71felutE2zRLGuloUzs4YLejvHonm81B9ejq5c+eCXpwzbj7zLI6hobh2Mr8HSldXHAICaCgy64SoW7SQ/l918JCUnANgMFB14CDOrW2FQl06dCD0/fcpXb4cVCr8Hp4pddeNDQ0oGrvkHsOGUfT55xgqK6XnYKi17RTXJSWhHjbstu/Lnx03y23fl0/3Xeez/dd5d3w7pnSLYPMTQngvvVBDabWWEC8XJnQJ46CFonpciCdtQuXJ09D4IE5ZjA1F+LoSF+LJsevF/HAmF09nBx7uG0W4rysXcyt4d5t51nXF8Sw6RngzvlMYNdoG7v3qBBfzREzUpbkPqx/pTnphlZScA9Tq9Hxz5AZv3NVWSs5N2JtSwFvj2rLq4e58ujeNvPJa7uoQyr0JEZRVaymp1hITaGblzFl9nj0poti17kwOHcNt2RaWDabfBHvx5Yj3IayziCUvbRB7XJvxwkI35WcRp7UeIxJvlVW6M+k72PGC6ERHD4Bh74q4z9L5Ql8v5r7v+hQeOyS/faitcwShjb/h1iMhdoSIWT0az4+5Z2wLpDcOik753V+LAm9tqWgAdXlQflxEdxj3d5H0K5RiZDOgtaDYa6vg7iXiPGI0wLe2+hRkHbVduwO4ObhR3WA7FhbiFoJaqcbfxZ/Xe70urb9z4h3Wpq4FRIc5tyqX1aNWkxiRSFldGSHuIRRUF/DR2Y+ksanUslS+vvA1L3V/iZ/Sf+KDMx9Qqa0kMSKRd/q8w+u9XkelUKFszCEGNx8sY3XG+sQS7hlOclEyn543O2isTV1Lp8BOjIwSlpULui7ASeXE0ZtHifWJ5akuYoxhVcoq2dz5rqxd5FfnE+wWLFOLBzFSmaPJobVva7v6SpboEdrjttffCf6UCfrPybZv3PbLt+jd0p95iTE8sfq8RNl5fEC0XRqlJRxVSh7tF82j/aKltdyyGjaey0PtoGRilzD83Z1wVClxUCrQWaijOzs0fd9Go5HZq86x47IQtYvyd2P9rJ5EB9rSEKMbaaELhsYyKC6Ia/kaesX4SdSkpnAgtZAn1yZRUasj0MOJJQ90pUO4Nw16A6/8dNnmeE2dbdJ3R7C3eardRUIM0H2WmD83WRJE9oewRlEVe1QQe2ttxotZThO8ws2JcMYBc3JuQspm2wTd0UXMt8vW3MQmqrEorpiUP9tNkPtiBsYLOqoJ1/davXajsIWzTtAj+8HEb2DTbDmT4B+Awc48VgvPFvQI6cGJWydQK4WfZJRXFJ+c/UR2XKW2kqQiM528oLqAufvmklKagofag4U9FjIickSTj/1i9xd5av9TFNUW4e3kzas9hWXP2tS1nC0QSsUGo4HvrnzHyKiRtPFrw/Xy63x87mNyNbkMbT6UR9s/yvQ2022o5huhoMAwAAAgAElEQVTTNsqC6PTydJKLkukcZD4h9gjpIaM/uTi4SEG+SqliaPOhsrkma1wovtDkdf8r6NsyAJUCLM0cBsQG4KBS0iPKlxMWbhi9Y0SH/L1tVyXGjU5vZNH2q3YT9BHtQtj5VD8OpxUTF+JJz2g/arQNPLkmSSo+7kkp4KPd13h1dDxD4oPYfUUEdhG+rjKmj5ODSjZfXlJVz/1LT0pFhBm9WvD6XW3Yd7Xwtsk5gP5OYkNnLxEoWaLoqhAO6jFb+PFuekyMsPi1hMmrhftDs65yBd22dhg8ajdBV79m8d1sO8H8f88QM2WxphS2zIW03cJ3d8wnIgDMPCK/T1218GePHiiCUJVVULzrFVEEBfsCeHcAay9xEz4e+DFrrq7BWeXMQ+0eItgt2Ea3Aswz7EfyjsgSfSNG9mbtlX67qaWp1DTU0CGgA0qFklhf+wUFrV7LF0lfSJd1Bh2LkxbTN6wvh/LkgXRpXSmXii/RJaiLVHi0hJPKya5VTn61rdisNTS7d9msVR87TsSypRR/9RXGmlp87puCW3dzR6zqyBEpOQfAaKTq0CFZgq5QqQh+4w1uvvAChspKHEJCCHrRPM6ljrIdRVNHyxsH+sZEW+XpidfoUXiNNgfuhro6br2ykMrt23Hw9yfoxRfxHD6MFmvXUrZ2DegN+Ey+l4wxd9k8Tn3uH6ej8t+K6nr7cZLRCO/vTGVyQjhGI8xZc45tF8X3qFukLyse7MbX07qw9eItmnm7MLNPJJuTbrL2dDbeLmrmDIrhod6RGIxGtl3MJ9zXlaeHtOJsVhlTl56UCp7bL+Vz4NkBXLDSHgK4mFvJ+E7w0/mbUnIOcDarjB2X8onyt40v9UZwd3YgyNOJgkrzHmqKRSP93fjoXjPj7etD1/nbzmto9QbaNvNk+YxuGDFKybkJWcW2vyvDHYgv3jFSt0NUf1iaCDcbC2qH/gYzd0GPWcJS7ZMOwraszd0i0TYVPn0j4b518vuzVxR1dBGz7dufE7ogHsEw7D1oNRSG/wUOvCtEOrvMEKJyIEYl194viq3uQSKBDmpj28gyjSG1m2if8QmCfXl5k2BkPrxXMEv1Olg21Pya970Nj+wH73AxD2+Nqts3LJpCtHe0Tazk5eTFqz1fJbMyk73ZewlyDWJ4i+E4qhw5nCe3t75YfJHSulJ8nX0JcRfntZvVN200TbI12RTWFPLGsTckO7PdWbuJ9YnlsQ5CtT1Hk8OtqltMi5uGg9KB/dn7ifSK5PGOYtw0pSQFa1wpucLIqJHcrLrJ3uy9xPvFM7fzXInCDsgaPyCESlWNzcMeIT0k3SWAQJdAor1FHjisxTAul9jmUCZYF51/D/6UCbrRYLsBGBrXhrcNYc8CUY2MD/GkU4QPRZp6nvsxWQomF01od1u/3NyyGkZ+cliacV95PIsdT/XFy8WRh3pH8tUhQet2VCl4YlCMdJvDacW0DHSXhNhO3iiVknOAjOJqVh7PYv6QVjw7LJZP9qahbTDQJ8afBy1mwzuGe0tWbJaob9BTUaOTLI6MRiMvb7ok0bEKNfW88fNlNj7em/M55RTYCWbdnf5Ar1MHV/OGF9LeXNX0aLS3MKHrg2LmxxQkBbUz+4pbosNkkbhf/EHMZ/Z7WlDRQVgcWcNyTVcngmz/VmKDs4TKQWygmx8XVmzhPYQaPAiBJpXabKNksrkwIcCOtZm9NRCB+M6X5YWAfwBKhdJmo+vXrB+PtH+E/Op8XB1dpdnzaO9oGS1dqVBKs0AAn57/VKpKarQa3jj+Bv3D+ss67OcKzlGlq6JHSA/aB7Rn58SdZFVkEe4ZLgW+1vQjEBtrrE8ss/fMlihB6eXpODk48VDbh2yO91Dbzjpbr93d8m4KawvZnL4ZX2df5nWeJ6PBv97rdcI8wrhYfJFLxZeo0slnsAqqbCmt/2vYn1qItdPimlPZdIv04+N7O/Halksk5ZTTPdKP1+8SRSlr54eyRspmVX0Dm87nUV6tZUyHUFr4u9EyyIOWFirFGUXVNsygC7kVKBQKljzQlbNZpVTWNtArxg8nBxXV9Q0cTS8mxMtFErsE4Xlu2eH/9lgm9yaEy/Q8msLg+Ca8xi3R7VFhc2ON0gyx/2yeI5JzEPTKnS/C1A0i2Dv8gUiCY0eKvcNggCs/ia5MzGAxu3j310JV/Vay2Od6zrH/PHa9Ald/Ef+/lQzrp8O8JNFdt0zwVWohRtkU0myTSBlCft3ru1OQ/WBjUMQgG4p6z9CetPRpSVqZmH12dXDlnlb3AEhetJYI8wjDaDTy7KFn2Zm5ExBetcuGLZP2rz1Ze7hQdIHOQZ0ZED4AnUFn09HXNNI6o72ipdl4EDPrzT3FPPmDbR7kSN4RafxldNRowj3D7dL1rR0t7KGh2NYiU19ejmuXLkR8/bWdW4BTtK2atlOM7ZrHoIG0PHgAXV4e6hYtpE43gEdiIl4T7qZi008AeI0fh0eimP80Go3kv/km5et/AIUC73smEvzqq7KRqJKly6j8RXy3GgoKuPn887h274ZTVCTBL71029esL7Vjd/X/kFBV34DBKEZ3TMk5wKkbpWw4l8vUHs0lv/G9KQU8tc48w3vsejGHnx9k0xD66lCGzLGiUFPPoWvF9Ijys3GzMDlj2HPpKa3WMrajF90jfSVxT7VKyYxezVEpFSya0J6n1ydTWq2lhZ8rr46Op06np7JOR6CHiCtvVdTylx1m0bhLeZV8cSCdBUNaoXaQj2A22InFrWn+/xCiBogGyk0LtouuBk4vhUELYeNj5obIxfViRrz/c+ZjzywX1HFdndAVGfSKaB6ZGJQuvuJ8cGIxnF3e+ALS4YfpgrXUY5a4XlcjNERMtmnbnhXJOQjG5ZY58OQFUWTdvVAwRuPHytXfDXrIOSUScf9G1fVbybBsmJmdde47mLlH0NktX3N1EZz9FhIXivOBdRPI9fdZftlrZOy9Zy9XSq4wYcsEdAaRW2zN2MqXQ74kyiuKvCoz6zTAJcBG/6itX1uCXIMoqDHHYIMjBnOt7JrMaxyQYtLFSYv5KvkrjBgJdA3km2Hf2MSOCcEJKBVKWXOnR2gPUkpSmL5jurTn9wzpyddDzXvzQ20f4mzBWRoax9PGx4yXdJIea/8Y1bpq9mTvIdwjnGe7Pisl9zPazECtUrM/ez9XSq6g0clZh+ll6U0WmO8Uf8oEPdDTtkru5uRARa0OLxdHIv3dpPlyEP7n+xspPxfzKpi75jx7F/RHoVBQq9VzOK0IP3e1pCy84WyeTIAur7yWnZcLmNgljBdHxjE4PohrBRr6tQwg3NeVI2nFPPTtabSN7ZtH+0Xx0sg4Su1sVCXV4of1xMAYpvZoTo22gRCvX5+b3JJ8k4U/iWS8U4Q3X03rgqezIzcr5FSszBJBsfFrwk5teJt/XPXTDKsZae9w++JqgXEw+6igKLl4Q/t7RdKsyRfdKr9osxd5p/vFnzVCOwkP4pNfCppPeA9IEDMtZB0T1czaUtEdm7hczGxaIrKv2EC1VbZ2bp2mij97iBksHufMN4AROkyxFaazhJ0T1u+FPWXlHZk7iPWLtaG6z2g7gwtFFziZfxIXBxee7Pyk7JiM/2PvvcOjKtfu/8+0ZNJ77xVCILQQekeqIgqioAgqoKKCgF2PFQv2hthARRCQJkVEepHeQyCEJEAS0nvPZDIzvz+ezJ7ZmQF8PZ73/L7nvOu6uMjs7NmzZ2fm2XdZ91pVcsG5en09hfWFxHjGYDKZeHz34+y/JjpTYW5h/DjqR3ycfIj1kgeWQ8OHsi5znfTYVeNK76DeXKq8ZDOvsy9vn90EfWqHqezO3S35n98afStxXnKbEIVCwaOdH+XRzo/avTbOGmdmd5sNQN+Vth6/1qIl/61Yd8q2mLLhdAFjOgUzrEMAX02xnTWdmBzGj0cs87QTe4RhMJqY+OVhiQb/xd5s1s/qQ0KQ/MYcF+CKj4uDLEDrFW1xjTCvrwDZpXXc/ZVF7fjenuG8eUcnAAqrbJWzC6oaGdTOn97RPjYOHtY4lFVOjJ99ayMJZg/ctki4DXQ1toq+Ja2VexdfGPm2/Heb51g0L/a/B3d8JQqNt7wu3y99iwgKgzqLNUSpgtw29OmqHFHc6/O40OJIWw8ufjDyLUHlNJmEum/aekG9H/IP0dn3ay8fD1Kq5aJEZg2PGyCt1I5I0XWgUWr4cdSPbMneQk1zDaOiRkmJeUpgCuPjxrM+cz0mTPQO6s242HEcKzomJecgPNDXZKzhoU4P8cmpT/j23LeAEMGc3XU2M5JmMCpqFL9e/lV6zoR40YGa0WkG58vPc7L4JM5qZ+Ynz5dE6jr5dWLzuM3sz99PiEsIvYOFroLZ4cEauj/BdFI6OWHUtdnPZMJYX4/SRcQYJqORptRUlO4eOEZH4dKvL1733kvlqlVgMuExdizuI0diMploOHoMfVEhrgMHovbyQunkJEvem/PyUPv6onRyIvjNN/GfNw8AtY8lAK/bvZuqlaukx1UrV+Havz9uQyyFlMZz8sDbpNOhy7iEupdlBM/YYH+NVHnZegL/t0GtUtDctrrZionJoaiUCtnMthltqfHb2lgC1zS1cCirjFFtWEl+rrbxmp+bAzF+rnx8T1c+2XmJJr2R+3tHSJpItyYFsWhPljTG6OaoZlRHcdwfHkxh05kCCqubGJMUKAmADm7nz+Hnh1BU3USYlzMbTudz5+JD1Da1kBLpzZdTupNX0WgjGpdT3oCbVsNjg2L5aKdg0Gg1SuID3DiZK+/yq2/iVHR92FEVcbrObLVCKYTV2n6H809afi5Kgy1Wau0H3ofAjnDfelHUbCgX4p7O3pDTxmFD3yCS57CeomB7foPYb/ibwre8VD6CR1Wu6Gx3myLWd4NO7mVeWyyo6WYF+OSH4NYPRaHBenQq/yTkHrJvzWvepnayfd8ufrb7/0WoUbP8wnIpOQc4WHCQjIoMnunxDNfqrnGl+greWm9e6/MaJQ0l0np/Z9ydhLiG8M3wb1h8ZjGF9YWMihrFhPgJ1DTX4KR2khJpgJ5BPSlpKOGb1G8kGnpJQwlfp37Nm/1EE62ovojL1Zfp4teFd/q/w1dnv6LJ0MR9CffRL6QfL/7xouyYhwsPc77sPIm+ounQJ7gPv9z+CweuHSDCPYJ+If1YfmE5W69sxd/Zn8e6PCbZxoG4Lyy/sJwL5RfoGdSTr4d/Tc8VtqPLWVU3ty+9+bX+D0ROhe3C+M2BK/xwOIfZQ2J5fIg82D/VZu7mcmk9lQ16GvUGmbrwmKQgFk3uZlf92NrbsUekNz2s7Mo+250pJecA3x28wmODYhkY74efmyOlrZ1slVLBnd0sHQYPJw0eTqJaYzKZWHrwKpvPFhDsqWXeLfHSolrbpOe5danSQnw6t4oPt1/infFJDIz3k80bjUgUi3e0nyt3JYey5oQ8SM+vtr12fxm6KpEYl2eJeZzBL1o63mVZwuvXO0aoXnpHCYsKM3KPCMV28xer5yMWuvz1MPJtIcihqxWqy2ZsfcZCWW2qhl/nw5wzts9XKOTJeeVVIa7h3MZ6TlcHxedFYUHrDmPeh8EviADZpU2l0mgURYNL20SgrLdNLv5ObLu8jTnd59hsd3dw59sR31LSUIKrxtVm9rx/aH/Z3GiYWxhRHoK1cbToqJScg+iI/5zxM492Eclxs6EZBQo0Kg39Q/uzsP9C1mWuw83BjRlJMyT7NUeVoyzoNVOFTCYTJ4pP0GJsISUwhUiPSLbeuZWD+Qfxd/aXLC1uhjMlZ1iXuQ5ntTP3dbiPMDehO2Ew2BYy7G37b0N2iX1l18X7smXCl9Z45bYOxPq7ciq3kuRIbyanhHMou0xmK9SoN7DiaA4LxnWSPddRreLr+5N5ffN5cisaGNUpiCfarMVmfLk3W6Z2vOJoLtP7RxPl68KtnYNYf9pSpfd1daRPjC8qpYIV03ty+HI59boWHll+0qYellP+JwozF3+13db7CTGmAhDaQ+hkmBF/nXlcsxqwNQ5/LhJ0axz6THTLzbh2XHRawlIsXRgAj3AxiqNUwvhvxUyiUm1h9Bz7xnKcglMimJtzVqyLq3JFUu/sa2vHlnH9URAz3DQ3LmoYjAZyanMIdglGq9bionHh7vZ32+ynUCh4tc+rPJz0MDqDjkiPSABKG0tt9i1tLMVkMvFTulxob3n6cmYkzeCNPm+Q5JtERmUGvYN6MzJqJACeWk++H/k9xfXFuDm42ax1AS4BUkf/RlDa001pA5cB/andtFm+saWFwpdfIeSD92mprCR32gPoMkTA7nnXBILeeIPAf7yE3xOPYzKZULcmvPnPPENN67GUbm5ELP8RbTtxH9MXFJD3yKPoLl1C6epK4Cuv4HHbrbLE3Axdpm1gqLuUKUvQXXr0oH6fZU1XOjujTZRrz1StXYc9qH3+RivW/0fR0Gw7KxPr58ID/aK4p4coeA1LCOCtrelSXKZSKmzGgSJ8bEcUI3xcSMuv5v3tGRTX6BjXJZgpvSPYklooCXfemhQkFTTHdg5mbOdgQHTtp/9wAkeNkpn9o1n3aB+WH8lBpVRwf+8InBxUkt3wxB7i/lhQ1ci81WfIKq1jaPsAHhscQ4SPC1UNzbyw4ZxkPXzsagWf7srk+dHtbajw5rhyzrA4hicGkFlSR58YH+5fIhc6BGiyY3/552CnIFJbJKjrYb2E8DAIkbSUmWKt1LhYmJkAEVbF+mvHbI+Xd0yMUgZ3FTpGVbki/gvrIeJVM9RaQU8/vAjSWi0M60th42OCch83XIgNW7+uo6tgWB1eJNbtAU+Lrj2IDr21PduJJYKBpbDDaFWoIG6EiJ3N9wcHN+h2v/i57agnCAeQvwkKhcKuQLFCoSDSI5KNt2+ksL4QPyc/qpuruWPjHZId2+qM1WwYu4EojyjeHfgu6eXplDSU0NjSiLuDO58N+YyPT35MaWMpY2PGcne7u8moyLBpRpk1hlZeXMnCYwsxmAy4Objx5bAv+WXcL7J9jW2F/BDNrdrmWt459g4H8w8S7xXPCz1fINIjkrWX1rLwuCXXOFt6lm3jt0ks0X8c/Ae/XRH3TPO8ur3XMDeY/hn8RybozS32F4DmFiPvb7/EyI5BMmGLbhFeFKRaunvRfi54OWtY9GuWTF3419RCHh5QxcTkMJYfzqGgWvyufaAbw68T0IIQabNGi9GEzmDAw1nL+kf7sLTV//fuHmGSHUdb/HQslze2CPGKM3lwKqeK/c8MxkGtJL+q0UbszWzT8fHdXXh/ewbnrlXTK8aHucPipX1u7xJsk6CfzbOdafrLMOgsFM3Si6DUwJAXhZDHiolCcA2gy3226sgHPpBXDo99Lejlbq3X+dpJYeFWcVl0tka+I+j07sG259HWWq0qVyTOxhb440Mxvx6YJATjnL1FEr9ysqBOKTViIR3UWkHL3gM/3y86aQ6uohsfP9w2iTfjjw/EfBC00qZuHvT9MyhqEBX5yqZKvk79msvVlxkQOoBJ7SehVCjt2hcBzEiaQYuxhd25uwl3D2du97lSgFptZ8Gv0lVhMpl4/8T7rLq4CpVSxbTEaczqMovR0aMlYQ4zPBw9eKnXSyw8tpA6fR0dfToyq8ss9AY9M3bMkObW23u35/uR3+OicWF45HC755pbk8tvV37D09GTW2NuxUXjwvmy8zyw7QGJIrXt6ja23LEFNwc3GyosQLPpb6TZ/T+K662TLTcY1FarlEztE8nUPpHSNpWdjoi6leqXVVLL5rOF+Lo5cmfXELpHeLHxcVsF6baosqOSXNXQDLgwpH0AX97XjZ9PXMPbxYFHB8Xg1OpmoVQqJGtLe2SVyoY/of9gzwc9zErw5a4fBE2x6BzEDIGhQn+By/uE325oD8HQUSjFP+vgQmmHhn/sG/nj0yvEejb8TZHkZ24X2hdjP7FQKMFS7DQjo01hoSZfdHlCk8V4UfU1sWa9G9nm/d48YHZ2tK91YjKZyKzK5IldT1BQX4C7gztv9XuLgWEDb3g88zyiGf1D+uPh6CGtNSqFitFRo1EoROEPqz+Jg0p0EzUqDZMTru/HHeBie08ubyxHo9LIaJevHnrVrmjkn3F60OfZUuMBarZvJwSoXPGTlJwDVK1Zi+ddd+GUlITK0zKSo7t8WUrOAYy1tZQvWULIu+8CUPLRx+guic6ksa6OoldfxW3IYKlLry8oQOXtjVKrxaVfP0o/+cSi2q5Q4NJf/p3znjoVfVExNZs3ow4IwP/ZZ1C5yYswzdfsvzeV+9+rCP2fAmeNiuIaHZvO5nNrUjCBHlpWzezFNweu0Nxi4P7ekbg6qnl2bSqldTru6BrC/X0i2ZtRyomcSlRKBdP7RRHl60LfhbslhmV6YQ1uWg3b5w7g6JUKPJw0hHo5caGghnaBbtL6ezavimnfHZPWvT0XS9g9f5DEPPr2wGXe+z0DXYuRzmGeLJ2ajI+rIw/9cEKyhEu9Vo3BZGLeLfFcLW+QknMzMopqcVSrWDG9Jx/tzKSoukkSjTMjIcidhCB3yut0XCi0tUWrbf6LOkf2UHpJrIlTN8GFTSJJ7nA7eLTay939I2z/h2A9Jd0tNETMCE2xPV5YikjMV9xlcdoY8IxoHlVcFTR5t0CxPjt7C+0Paxj1ojM/aqGIR82sqOELhJbILivm1NanhNBcaHeosTP2WFsoCg3n1lpU2cP7CDapQgEzdgm7Yl2dsINrrhNjUPYKGS2299O/CoVCwX0J97Endw/NRvEZ7RfSj3iveOn3wa4iDt92ZZvMK71aV83WK1uZmjiV1w+/zppLawDwdfLlh5E/0DOoJytvXUmLsUVKwtt7tyfWM1bWkb41+lYaWxr5+OTHUvJe21zLp6c/5dvh38rOd1L7SWy/ul06127+3UjyS+Llgy+zKXsTILrq8/bNY/3Y9ezJ2yN7flljGedKz5EcmExTS5OM6QWwMXsjHo4eNkXmbv52RAT/h/iPTNBrmm78YcwurZMl6K/clki9roUDmWV0CHbnnVZf9KoGe0GinqRQT7bNHcC2tCIc1UpGJAai1ajYcaGYJX9cRq1UMnNANAPiBa3k/t6RzF9jERoY1TFQmucJ83bmldsSZa9xMKuM3RdLiPV3ZXy3UBzUSrafl8/MFtU0kXqtiuRIb+L83WxsMAbFi0TM09nBppNlhoedmc221KU/DzvWRG1xaZtI0A9+aknOAc6sELM/1n6ObYUuTEZLwt7SDKsmQ10rPezk98JPeNir9l+3w1jxGmYk3CoW9e2visoliMC67BLc/wscWWwRhjPqhQhIxzvFXNC250RyDmJB/O1pkaBL52mSz6inbWhzMvau0V+lfNnCHJTO2TNH8hk/VHCIxpZGpneaLu33w/kfWHJuCUaMTEucxvRO05ndbbZEC7dG/5D++Dn5SQuQWqFmbMxYdufuZtmFZdLbWnx28Q1t28bFjmNE5AiqmqqkAH3b1W1Scg6C2ropexOT2k+ye4yMigym/DZFoiytzVzLyjEr2XJ5i2x+qaKpgv3X9jMm2o6iKfYF9v7boFYrQW+bnD3YqndxOreSlcdycXZQM61PJJFWY0HW6BXlQ3KEFydamUjuWjVTekdwJq+KiV8dlmYS1568xoZH+0hWPwCp16p46Zc0skqELc/bd3bCXathYnKYJBoHogjaOdSS0IzsGMTIjvIkr6G5heYWo11VeMvr2ekutIVCBbRZ+92sClseIULw0RoHPxVJuxmDX4KBT1vmF8GivtsWbb3J1VpxDi4+MHmV7f7Xg0+sKDaaodQIRwkQa5Jn2HUYPDf/LoS72afBG0wG3j32LgX1gvZf01zD64dfZ3vIdkyYWHhsIRuzN+Ll6MW85HmMiLSwDeqa6yhpKCHKIwoPRw+WjVzGsgvLqNfXMyF+Akl+QkDp4aSHeff4u9LzHk562O65VOuqWZ+5nipdFWOix0gBI4DeoOeFP17g96u/o1KqmNJhCvO6zyOvNk82kmONtrOT9qAvsK8n4hAqmHAtxbZCc/qiIpyS5BarxgZb5po1xbz5snwEyVhfj764BKWLC9dmzaLp/HmUbm4E/uMlPMaOJeTDDyhfIj6jPtMfwilRHmMoNBoCX3qRwJdevO57c7/lFiqX2Tp46C5fhqFD7TzjvxupBTWkFojYYOeFEhbd242kUE8+myT0G5r0Bga9t1dq+uy+WMKX93Vn7aN9yCqpw12rxt9dy6HsMpvxx90Xi5ncM5y+sb4sP5LDnYsP0dxiJMLHmWUPphDh48LWtEJZUbKh2cCui8Xc2zOCouom3v7tohTfnc2r4ou92UzrEynzawfYfr6IebfE0yHIHX83R5nw5qB2IqaN9Xdj0WRLAnLuWjWf7LpEZYOeicmh3N0j/LqjRmZr4L8F+xeKJPienyDpLqjMEQKeOYdEsn37Iph1SOxblSscd/JPQWQ/GP0+3Pox7H0H9I2im514B3x/qyU5Bzj4sRgrGrfItpEUNcDShAIxhx7aXXTLx7wv3/eYHU2K3MNi/6SJIvk3wy1I2MNptK3aTZsE+ynhNjEqdWqZGAcd8rKIP5uq4etBwmLYHv6iMF+4azi5dZbiZYBTAAqFgi7+Xdhw+wZ25e4i0CWQoeFD0Rv1MvE1AK1aa3NMJ7UTV6qvSMk5iCR4adpSXu3zKieLT/LMvmcoaSwhxDWEjwd/zDfDv2HJuSUU1BUwInIEo6NHU9ZYZjOqWGbFDjtccJjMykx6B/dm7di1bL+6HT9nP0ZHieaR2T7NjMzKTMobyyW9EjNUCpXExlQr1bg7uMuKDj5aH5zVzjYJurfTP880+o9M0O0JU5jh6qimV7ScFubn5sh3D9hW0+5KDmXD6WvSohfh4yw91xxEmpF6rYqHfzwh7Xv0SjnbnhxAjJ8r47uHEuihZVd6CXEBIum+HtadvCZL5v/IKmPR5G5E+brI7DnUSgXh3mCBHywAACAASURBVKKroVIq+O6BHry9NZ2cigZGJgYya3CMzbHNOHalguNXK7AnMH89ddKb43qJp9Xfwj+hdde2r2GyVYFPflBuDRE7zCL6Vp5pSc7NuNJK18s9KiqTFVdEIj7mA7EQu/qLRTu0h+iUgxB+s8blPcLP2N4ManmWSNCr28ztmh83VsIvjwnxJp9YsfBH9hVBccn1lR6l9/83IcErgaL6Iik5N2Prla1Sgn6i6IRkTwTwyalPSPRJlOYx28JZ48zy0cv5Kf0n6vR13Bl3J4m+iTZe6CBUM80Jer2+npUXV5Jbk8vQ8KEMDBuIk9oJJ1eLpkKVnRuKvW1mrLm0RjZPdLHiIscKj+GttV0Mzds0Sg2GNl1CDTcXFPtPh5+rIzWN8hucgwpu7xLC+YJqJn51WHKk2HS2gD3zB+HhrKGivpkfDl2lpLaJsZ1D6B3jw4oZPfn9fDFVDc2MTAzE313LU2vOygSDzuZVcSKnkpQo8XcxGIWLhbmw+GtqIR5OGt66oxO3dAhg2YMpbDpbQJCHlgf6RskS+7b4Ym8Wn+7KRNdiZFTHQD6caH8swvs62hsy2GFckLlLzPF526pnA3Do0zaPPxMJ+si3hJNE8QXRbQ+wY2E56DlYM82yBg58GtQ3OU99k3jNvKOiE9R3juj0XDsuuuZqJzHnbraDNKPOlkr+Z9DW1tAMtVLN1Zqrsm0ljSU0tDSwKXuTZLnT2NLIcweeo6t/V/yd/VmfuZ53jr1DY0sj0R7RfDHsC6I9o2WWPWZM6TCFrv5dJZG49t62Ipx6o577f7ufy9UikV1+YTnLRi8j0UckppuyN0muDy3GFr5L+44hYUNwUl9f36Wmuea6v5Pev48PhuI2gpNKJYEvi2KN+5gxgireGiCrvLxw6SNotqYWcR9UqNVoEzugTUqiKTVVOobX3ZZRCNdBg2g6b7mPOERF4RAVSeGLL0nbjbW1FL76Gq5DhuA+ahTuoywuHMbmZopee42azVtQBwQQ8PxzMsq7samJ0o8+om7vPhziYgl49lmce9gvtKL/+7px/6n49VwhL1Y1Emzlb378aoWMkQnCcWhkx0BZwyja1xWVUiFrlpjV1Ksb9Sz49YK0ruaUN/DB9kt8OqkroZ62n2Wzv3pOeb1N8+VKWT2+ro64Oapl4p1mfSYHtZLvH0hh4baLXKtsYExSMFP7RLL5bAH5VY2MSAwkyteF6kY9k789IjkAncypxF2rIc7f/liM8U9ZafwPkHdEMC5HviXE2K62KonnHoZfHhWK7gDrZlho8OfXi/V24jIhUmwNXZvvvaEZWnSC/v7rfBFbth8jZsR7TBdMpbOrRIx5y+uiWQSiEHD6RzFrnjJTzKu3hdnvPO4WuGclnP1JJOJ954iGltnBo8d00ZE/sxL2tAoYV+eJgsOT58R94AZx01+NMa2Tc4DixmIMRgMqpYpw93Ae6PgAO3N2Mmr9KMobyxkWMYzX+7wujRWNihrF8gvLya4WVPxoj2hGR40msyrT5rXMSe+rh16lpFF0z/Pr8nnzyJv8OPpHnk15ltTSVE6XnOZMyRm6+Hehd1BvDhda9AFujbkVgA9OfMD3578HxKjS+wPfl9TgzUjwSZAKyyCs47y0XkzvNJ0zJWc4V3YOR5Ujs7vOlhpfaqWaed3n8drh1zCYDGhVWp7s9iTvHLMVlTX9DW4F/5EJekWdLY3RUa2kZ7QPTw6Lw8NJQ3mdjoPZ5UT7utAxxL5ie69oH1bN7M36U9fwcXVgap9I2ay5NXaml8iql3qDiT0XSyR7tL6xvhL1EuBCQQ2L9mZR06hnUkq4NJu07PBV2XG3niuktFbHY4NjOZFTQVp+DQ5qJc+ObC+ptQPEB7jZLTK0xdI/rvD6lgvX/f31hE/+EhQqUUlsqhIUcjMVtNejIvk2z210GCdEjazRaYIIiC/+KkTizPM1AF5RQuzN2js9uKtYRFffa1FYPrtSCDcNX2C/u+4VIfcqdvEXi2m70UIp3gyth2V2KfEOseiakXin+H/X6xaKadklEXDPPS/ec8EZUVBQOQhqvelvpHi1QXuf9rg7uOOsdpZVF4NcLN3GM6W28/dnSs9ICfqV6iu8fvh10ivS6RnYk5d7v0ywazBP9XhK9pyegT35OtVSFVagICXQ8hmcvXs2x4rEnNeGrA282e9NxsbIbXuGRQzj09OfSgrMWpWWUVGjaDG28N7x99iUvQlvrTfzkucxNHwoaqXtkqVWqrkr/i62XN4iBeiDQgfRK0jQkq3FTMww8H8z6EMTAsguldtndW8dsdl4pkBmF1lR38zO9GLu6BrCpK+PSCM0q47n8d20Hgxq5y/NQZqhUdkm1NbbCqoabYSUjlv5/w6I95NYSOb95/98liNXyukU4sF7EzrTLtCNjKJa3t1mSSC3nisiOcK+z33HG7hz3BD73xH/uk0VVj1t0XZe2ZpFEzNE/APRqdnxiphn9EuAEW8Khs8TJ4R9WlBni8XjjbB1vmW+PWsnVOeKbtHD+4Utj4uvxQu9xSwe5GDrw/snUa67vvDe4LDBUiIOkByQjJuDG2dK5OtMi7GF82XncQp0kpJzEH65n5/+nLf7WwT2mg3NnC45TbBLMGHuYXT07WjXC92Mo4VHpe8+QLOxmXWX1pHYWyTo9gR7sqqymBA/gc5+nWVWOmY0Gm6ux6KvsDNn6OmJS2+xlrr06kXo4i+oWrsWlYcHPg89hMrVhZIPPqRi+XIUCgXe0x/Cb9YswpcuoernNeiLCnEfNUpmu+b76CNgMlG7axcOUVH4z5uLQqGgOTtb9tKmhgZBd4+Pl22vWLqU6nXrxTnn5ZE/bz5xe/dINPvSjz6m4gfBhmrOyUGfm0f0pjYF7FYoPf9PJO5mUChsR3/sif0Ge4oYrqS2iaZmI+E+zgR6aPnHmAQWbsugUW8gJcqbRweKhktpbRNNenmCm1shvtMTuofx67lCyR7z9i7BDIgT62eXcE+Z3hHALR0CcHJQ8ca4jry44Rz1zQaifF14dmR7zuZVsfbkNdyd1Lw3IUmKNR/6/ji7Lork6cMdl1gxvSeV9c029rzbLxTz0d1B9mTdqNf/jTGmGaWtftV5bebKzY9bdJbk3IzL+yw/V18TrE2/dqI5tNlKx6f9raD1FHpK9a12ZamrxBo74k2RlLcV/Sw8C0tHWITbUteINX7QC60z6CoxOunXTjj7FJwWHfPxS0DtKMaetlrFWzmHYNJKi7q8GQadEI3zvn5DDoCQf97yy4xeK3oxo/MMZibNpFpXzfMHnpfGCH+/+jsR7hE80fUJAFw0Lqy+bTX78vZhwsSgsEE4qhxJ8k0ixiNGStxBMCz1Rr1NwTe7SuyzIn2FLBF+psczfDBIJOJZlVn0C+3HhLgJ1OvrWZFuYcwaTUa+Pfctt0TcIjvucynPUdZYxtnSs4S6hrKg3wKUCiXeWm9+GvMTuTW5eGo9qWqqYsrWKZwtPUuSXxJv9XuLbeO3kVGRQWe/znhqPe26Fx0qOMSAsAH/1LX+j0zQrcUrzNCoFCx7UCQPp3KFp6R5bvuRgTE8N8pSlc+raMBNq8bT2YGUKG+p43MjRNuhf0b72aeEVjfouefrw5IS/IHMMpY9mMKAeD8c23iyq5UKHFRKPJw1bHmiP5dL6/BxccTD+cYdwPMF1XyxN9umALB4X/YNn/e3Qu0C8zOEKJF1At5+DMzYLcSJvGMEfRzEHE7eERGgekcLsY1oO7OMDs5iIdv8pKhext0iBOjKMi3JuRk5h65/fsMXwMp7hA2GxkVQkpQqcT5N1SIAdvGFgc8KMTgQ3Xj3EMusaf/5Ynvecfmx60uE6nJAoqhwFp4V3bePu4H+T9Bs/yKOFh3l4S4PMz95Pm8fe5sWYws+Wh9md7VQ17v42XYXrbc9s/8ZLlaIG97uvN2olCo+HPShzXNSglJ4qedL/HDhB9RKNdM7TSfBR7Ak8mrypOTcjHWX1tkk6L5OvqwYvYKVF8Xc0cR2E4n0iGRF+gp+uijEoer0dTy972l2TNjB3e3uZlP2Jimh7+rfleTAZJQKJevGruNY0TFcNC509rMkOfaS8f+juEOYl+1ccVyg6HrY6zR7uzpwOq9SSs5BNAZXH89jUDtbbYNpfaLYfLaQutbuTL9YX7paaWwEeWhtxIbs2Uea8dIvaRJtMvVaNXNWnWbbkwO4WGTb6cwoqsXTSU1VozxwTLrB8f8UTv0AXe6F8DbdkH5zxfiL9WN72PU6HPtK/Fx5FSqvCAqjd7T41xYmkygIZu4Qa0nvx0XR89xa+X7n1ooEHURB0/r1Drf6hfd6BJKn81fQrLu+ZsNTPZ7CSePEkYIjtPNux5xuIrjt4t9F6lqDKKR19O0oiQJZI7fGUlDJqcnhwd8fpKShBAUKZiTNkAK+FmMLhwoOoTfq6R/SX5pH16psqZTW9MoBoQNYnm4RbdIoNVJB8sthXzJ87XAbmxx7wj9tYSy3U7iolq/vboMG4TZokPS4dvceyr8RugMmoOzTz3DunoxLzxR8HpR384w6HQqNBoVajd/sJ/Cb/YTs966DBtJ41lJc0ISG2rVsazwtL5aYmppoungRl16iiFm3X+4dr7t0CX2RfR/4+kOH8JlsfwTp/yBwV/dQXBzVvLU1nTO5VfSM9uaxwbHMHBDNNwcuYzJBrL8rMwZE88aWC3x/6CoGo4n+cb58NaU70/pGcVdyGDVNelRKBecLakgK9SDGz5X4AFcuFVsEPkd3Em4sTg4qVs3sTXphDVqNCh9XB5YevEJprY7bOgez/KGefLgjQ8yNdwlhUooYWxnXNYRhHQIoqm4k2teV1PxqJiw+JDFRfzldwK75A8mtaJCScxCaTkv/uMK8W+TFILB04f8Fqbh9xAuBSGmG3Axzh1rtKER6zYk8COtfEF3x40sAk2jETP4ZXANFB9s/QRRly7MsybkZ5tjy9Aphy6lvEp3ygU/D2dVypfW6Irj0u9AyMusZAaycBBlbW493UCjHj3kfTv4gf62MrYL9FNRZNJ4kKETzyydG3BuOfGFpfFmjLSvgn0CTsYnPTn9G94DumEwmG42f82WC0bMxayNL0pZgMpmYljiN8fHjpX1UShVLRixhRfoKihuKGR01mr4hognWJ7gPhwoscXv/0P4AfJMq12r5JvUbpnSYwhNdn6CppYlmYzMKhQKjyWizdlszKPNq89iSvQUntROfD/kcjUqDs9qZy9WXmbN7DgX1gkb/YMcHUSqUPLHrCampdbb0LP84+A9+GPWDzAWprT0cyO9pfxX/kQm6fdEiy7bPdmXKRNW+PXCZGf2j0KiVTP/hBMeuVOCgUjJ7qFzxvbxOx2ubL3DiagXdIrx45bZE/NyEst+YpCB2pBfza2ohCoVYoAfF+1Naq2PpwSsUVzdxe9cQBsb78UdWmcymDUSnfEC8H08MieWh709Iqu8P9I2SJePRfhYqVF5FA/PXnOX41Qq6hHny/l2difFzpaqhmXu+tlCODmSW8eNDKfSP+/usFv4U3IPFDE3b7jiIjnewVVUv4zchvmZoBhSCPpRsZcFVlSfEkrwiRTcq7haYmyYqo5rWQEzjJOhFjVaq/CG2VlGW33WDJ9MEBd07xpKEg6A9taU+gXitwc/bbo/oDcXnLI/dgkSnH0TnKqyVLugeBOX/ugTdRS1ujBPbTWRI+BDyavNI9EmUAlmA5MBknkp+iqVpSzGaxAy6OVit19dLybkZ1jPiIGwtPBw9cFI7cXf7u+2qNTtrnFEpVDL1TeuZzpyaHHQGHfFe8UR5RPFCT7n3btvX1Bv1pJWlMTBsIBtv38jO3J14OnoyNHyoJGanVqrpE9xHek5GRQafn/n85hftvxQZdhLbM7niszmpRzjrT12TAsFB7fwYEOfH5VJb5Xez00Rjs4HtF4owmkwM7xBIu0A3ds0fyPbzRfi6Otoow6tVShZN7sZz68+RXVrH4Hb+PD86gXpdC4v2ZEke7I8MisZRreJUrtxt42JRLU16A71jfHBUK2WCRoPa+bHroq3Xvb2u/v8Y1XlAmwS916NiPTPPPkZeRwgva6f8celFsbZ5htnf/4+PYNdr4uf0TUKZ/d41okhorfBuTxwza6egflofy9u+av7N0Gi6fjfZUeXIvO7zoLt8+93t7uZq9VU2Zm/EW+vN3O5z8XP2w9fJl0j3SFmnxNpL/evUryWBIBMmvj33LRPjJ+Kl9eKBbQ9I3ryR7pEsH70cD0cPugd0p1dQL44Uii6Zl6OXTMeid3Bv3uj7BisvrkSr0jIzaSYhrkJIytXBFQ8HD5sEXfFntEGUdhh1CgW5M2eizy/AfeRIfGc9ikJlKbw3pZ2zeUpTWhouPS3sI0NdPQXPPUvdrt2ofX0JeOkl3EfYimb6zJiBSd9C7Y4daCLC8Z83H4Wdc3Lq1o26fZbum8LJCW1CgvTYMS6O5isWNo3K2xu1t/3GhDrEzmft/0C0jxOTe0US7efCoHh/HvvpFL+12qgdu1pBWZ2Ot+9MYkqvCMrqdHQO9SQ1v5olf1iu+4HMMn5qdaxwcVTzW1oRz69PRW8w4eao5tupyXz/QAof7bjE1fJ6RiQGMjklnE1nC6hramFkx0ASgtwxGk3c9vkfnG+diV/yxxVWzuwlWWdeq2xgzqrTZLcqtz8xJFZyBVpzIk82Jppf1cj+S6V2NUhMJogLcGP20DgW781CbzDRM8qbaX0j7RZO/3a4BUPKdEEBBxj7uVBSzz0sRn9ut7r/j1sM62eIZDswSYwh5h0TVmZm5BwUfud9noB2Iy3bvaOFJ3qjFWMmNFlYbG58DKkUsWeBSOqd7BSC227TN9k6aJzfIBJ0bRuml1orYs/khwQj89waUagd+rKlIDviTWE1/HEn25HRujbFhb8BaWVpTIifgKvGlTq9JS5IDkwmrSyNlw5anElePfwqMZ4xdPHvQmFdIcvTl1Otq2Zc7DiSA+Ux+lv93uLDkx9yruwcyQHJzO0uit1tldzNSfF3ad+x+OximlqaGB45nLf6vcW42HEybZEpHaYAIu68e8vd1Lcq+/986WfWjV1Hi6mFh3c8LHmzX6y4iFal5b4O95FaKrekbMu2MhgNkgWcNcw2bv8M/iUJutFo5NVXXyUjIwMHBwcWLFhARIRl8H7fvn0sWrQIk8lEYmIir7zyCt988w0HDojZkZqaGsrKyjh48OD1XuKGcNeqqdPJ/5heTpYkt21y3GI00dBsYM3hHI61UiybDUY+2HGJMUnBUjXw2XWp7EwXH/SCVNEZ+r6VVq5pDTZfHN2ISqkgwF2LwWjinq8Pk10qPgzrT+ezZGoyoV62VKew1nny/nF+7Jo/kD+yyoj1d5XZtbXF8+vPSed7OreK+T+f5ZfH+nIgs8yGcrT1XBH94/x4eEA0C379+ywXbogGq0XB0CLsKErSRXLdNoDd9bpVxdEkHnebavEDXna7Rbijy70w7gvBI9NYdU40TkJV/dd5ojvVbrQQpbsR1A7yQoE9GA2i+llbAO3GyMXszBjyD2ioEAuubyyM/kB4ubdF2w7/34wzJWfYfnU7wyOH4+vkK/n/gqgcOqud8XHyYWriVKYmTrV5vovGhWiPaBldtJOvEBksayxjzu45pJal4qpx5dmUZxkXO87ueZhfY2naUum4D3d+GKPJyPMHnmfrFVE1TglM4fOhn9vMgnb268yOnB3SY7VSTQcfMb/r5+x3XRE5MxpbGpmxfQaVusob7vf/B/y71su2HrwAFwpFgu7hrGHr7P4cuVyBk4OK7hGi8x0X4MaE7qGsPSkoXT4uDswcEE2droXbP/9DWusifDLZ9Fg/Aty1TOkdKR0/o6iWzWcL8HNzZEL3UJIjvdk5byBGo0maMX/sp1P82uqqcSi7nJLaJt68oxPJEd7sTLck3R1D3NFqVGg1KpZM7cHHOy9R3coYGtUpiFk/nbJ5f6dzqhjd6Z9IMBw9LHT1jG2QfwIi+oht4b3EvxshIFGMwJjh7AuuAYJeeeBDkYBH9oU+c8TaJOuWIIqU9eVCRXjNNGEhpHGBkXYsKAvsWEneVA/DPoJd7F+zMevH8GLPF+kT0sfmd2qlmhd7vciLveRrsEKhYPGwxXx+5nNya3IZEj6EaYnTpN+XNsjXSKPJSEVTBadLTkvJOcDVmqtszNrI/Yn3o1Ao+HLYl/yR/wdVuioGhQ2S7B3NGBc7zma9yqjIwFnjjIOdmX+VPYujNnCMjUV3vs01NRio3y++m2WLFqF0dsLnoYekXzunpMAXi2VPcU6Rz3uXf/UVdTt3AdBSWkrBs8/i0qsnKg/5e7LXWTdUV1P83ns0njyFU9eu+D/9FN4PTEOfn0/1li1o/Pzwf/452bH8n3mG5txcdBcvovL2JujNBSgc7OsgOPj+Lxf67eDfHWPag4ezA9P7CxZMi8HI7+fl6+uvqYW8fWcSYd7OUrx3taze5jhXWrfpDUbe/PWCNGpUq2vhnW0X2TCrL+/d1VnaZ8LiQ5xtFb/8cEcGGx/vR35lo5Scg4hxVxzJkeLJh74/ITGh0vJrMIHUCXd3smVnejhpiA9wY3A7P/a02vY6qJSSoOi8W+KZ1ieS2iY9ET4uLNqTxXu/29et+Fsx9jMRAy4bK0Ypu02Faa2ibc0NokCZd1TMf/efD4+fEDa85mZM/knbY5pdf9K3CBalk5dgRN31HWyZa4ktB78Iaeuw4QnkHIL+88TaXdEaR8UMgeghYi694jJEDxYq8G5BIq40w6v1Mzz4eaEkbx5JGvCUxQL4zq/g1o/EyGRTq5Wx2Ynoto+xKzr8Jywj7UGJ8rpswx6BPXDRuPDJ4E9478R7FNUXMSpqFFM7TOWHCz/Y7H+48DDtvNsx5bcpUiK85fIWvhv5HV39u2I0GdmZs5NLlZcYHzde8jk34/4O9/Pp6U9lj7Mqs/jwpIXd+fvV3+nq35WXe79Mr+BeZFZm0je4L90ChKDhhswNUnIOIibed20fIS4h0jmZsTdvL/d1uI9uAd1kbFDzscyob7H9DgOSyOk/g39Jgr5z506am5tZvXo1Z86c4Z133mHxYnFDqqur47333mPZsmV4e3vzzTffUFlZycyZM5k5cyYADz/8ME8//fRffn1fNy0F1XKau6/VvPaklHBOWnmf94v1Jczb2aY7ZDLBlbI6KUHfnyn3jz3Q5jEgEwU5lVspBaxmrDlxjS+ndGdq7wiWHcnBZIKu4Z5M6W25uYR5O0v0I4DC6kbe2nqRi4U19I/z45mR7dBqVLL3AHAmrwqj0SQt/tYI8xbnNb1/NInBHhy/WoGHVs0rm+Xz6O7av1Fhs7Fc0I2iBsCmJ4QABghVzHGLoYuVRU5jm0RKVyvE5JQqOPiJXFXzzE9CWMnTjrJwzGCYfVrMetvrbujqhMiGWTBuyEuWyuaRL4X/pKObmBWKGya2r7pXiL+BmB994DcLPcoMrTtMWHLj63H1D2i6/izn34EqfRXz983nM9VnDAobBECDvoHZu2dztOgoaoWa+xPvl6qS9vBO/3d46eBLXKq8RHJAMi/1EpXQL89+KQXIdfo6FhxZwOCwwXg4elDXXMeiM4tILUulu393Hu3yKHO7z2VU1ChyanLoFdQLD0cP/sj/Q0rOAY4VHWNT1iabLvzkhMlcrbnKpqxNeDt5Mz95Pn7OtoFhU0sTC48vZHfubiLcI3g25VkSfRI5XXL6/4nkHP5962WLnfuuta6JWqWkX5yvzT7v39WZyT3DKalpol+cH66OalYfz5WtdTnlDfxyJl9mx3Yqt5J7vjoisYPWnbrGL7P6olQqpOS8xWC0KRxsPlvAm3d04q07OmIwGjmUXU5SqAfvjLd8B/vF+dqcqz2NlkvFtrY/fwresRDcBfo9KQKr3QuEmq4ZwxeIrguIme+CU2J9atvZvuUNITJUeEYk5mM/F4n4zzPgQqt/6+U9UFssOimuAfKE3sFVjPjED4d5F6A4DQI62u/YRA0E3pBvi+hnUZX/H8Dedw8gtzaXp/Y9xc67duKsceZwwWERnJlE18JMW2yLULdQ3ulvK6wDQujHWvgn1jOW9t7tOV9uW1ywDrZUStVN7d2sn/fIjkck6qI9n3d7Xr9tofS0o2nQ5oNXt2+/LEF36dWLgBdfpOK770ClwmfmDJw6yZ1Wms6nyQ/Z1IQuOxunrl0pW/QFVT//jNLNDb85s3EfLu+sF/7jZWq3C2Gs5itXMFRVEfbFIoJef42g11+z+z4cQkOI/mUD+uJi1N7eKDStSZqTEzTK2RMufeyLif5v4t8dY9qDq5U6uVqlJNjTiWuVlmtnjstqmvSkF9SQEOxOvzhfnB1UMkbniERBndW1GG3sJkvajG/uv1QqJecAZXXNrDyaKx3DGmYrytzyBtmYEsCOC8VSgj61dyQbT+dLNsJD2vtLY55fTUnmt7RCrlUKkbgmvYFp3x2jqLqJcV1DeHhANA3NLXy221YE7F+CKwfEyJB5ZGbrU8IGLeE20agxFzivHhDjkHd8KWdKxgwRHuLNVtcjYaxgH62+17ItczvMOWsbW4baYWiGdhejkbOOCFteR1dBnd/xDyEeCmIdv3+jEDFeP0M4Ajn7wIhWHY7IfjAnVZy3X3sRk65/WNwL2o0SlsMqNWx92qIin3sI1j6I3cECe9ahfwL2OsNhbmHM6DRDapikBKWw5rY1sn06eNuKoSZ4J3C44LAsETaYDGzK3kRX/668dfQtVmesBuCr1K94o+8bsoLqjKQZJPgkcKr4FF38uzAgdIDkR26NjIoMlAolIyNH0tWvK2dKz5BXm0eYW5iw7GwDB6UDoW6haJQamV5RlIcoPr3R9w1ePvSyEKbz68JrfV8jrzZPzKT7JhHuHo67g7uNqKh1c+yv4l+SoJ88eZL+/cXcQJcuXUhLs9xsTp8+TXx8PAsXLiQvL4+77roLbysqthxyAAAAIABJREFU1fbt23F3d6dfv5t75ep0OtLTbbvBAY62H8YAxxZp30RneG1oIIdz6wlx13BrO1fS09NJ9DBgZZiAq4MSj+Yy0tNFlzrKU0NGmWWBjPTUSMesaGihXm8kzMNSda6stp3bU+rrSU9P5554FUOCw2nQG4n0ciD/Shb513mfT/6aL71uZkkdpWXlzOrlS4KfA6cKLDeABD9HMjIu4gjc1t6dLRdFZbSDnyMpXpZr5QncEgyXy23pqk5q7F7TmyHGwQeHZtvks/TERiqqHYhPXSWr6zXt/YgrjlYiOOGj8TtvSXCrIkZSmCmqj2HV5ciNiExkX0qn2bUOt2t7cKo4T4NfV+qCb/6ZCT7yKh45rXORhWeoKbhEfr+FuF3bS+hBywypceU9ZI9Zh0pXRfQlq0WguY6q39+msOfL0iaFQYdJ5WjzWur6IlAoaWn1HvdL/Rl7X9kWjQuZf+Ga3wirz64moE5Qin8p+IWjRcJSosXUwtK0pbQztSPKJYpGQyPHK4+jVqhJ9krGQSk+vwviFmAwGVApVFTmVlJJJecK5NRMnUHHgdQDxLjG8H7m+xyrFFXG1NJUrhRfYVb0LADCCafgcgEFFHCqxLarmZqbSpLJtto40XMiE5MnigeN9j+Xy/OWs6lQeFlWNFUw6/dZLOqyCH2zHgUKuzcYM/7K5/xfgX/XehnoDG002tC2fv+bDSY0yusnKU5AhAryLgvl1Rw79mW5+YWkp1teYNEfJVJyDmKOfN3+M3QMkM8P+zipKKm3rOG+TkrpvJ/p5Qa9RDLVXJZHum2NFLi+gqqzqfGmf/f22PYgCiJuozr+bqgEKs4Tf2gR1qVM/f6PyfIahkNNDuF7n0DTWIJJoaIk6VEq2t8nP9iAL1E1lWNw8ACDGs6fo336Ztlrtpz9mczoh3CKmUJY/ilU+npMCiXFHR+mMuuq1Z4+cLUQhSGHwJPv4Z7zOwatN8Vd51AbOhjP5OfxvrgcBSbK299HbZ2WthOjJuDiTa5JfuP17k5Qq69l15ldOKmceCrtKYmKeKTwCAsTFxLuHM7l+susyFtBma6MPj59uCvkLmk0BaBGX4MCBW4aN+KIY07MHA5XHMbPwY/bg27n4sWLhOnDcFe7U9MiAiGtUkt7U/sb/j2LmopYfGUxWXVZJLgl8EjUI/g6+rKpcJNMLLMtvR0A459YI8qu8wG0QoOvj+1xkruLf0ARUNT291FRcMhSpMDVlRylEr76Cj5vpe2WlJA/dx75XywCfysNiL17ZYeq27vX9vX1eli5Ck6dgrAwuH8K+LUWYczCdyX2abFXDx+xX/z+X8S/O8a0h+ziKk6fO09xbQuhHhpmdPNg4f4mGvQm3B2VTEtyYfnOk7y5t5jGFhNatYLnBvizYGgAq85V0dBsZHQ7d/wMZaS3Lmy9Qp05nGcRduwX5ig7nys5tt27wpJS1OFGeoU5c6T1ua4OSgYFmUhPT6epxYizRkGDlVibr4NBOm6zwcTnYwI4VdCEm6OSTgFaFqw9wrrz1SiAuzp5ckcHD2qLc5i6NpcanVjT3/ntIvWVZfSLcEGnv7F+w1+599pbl4tqdAS20bOoOraaQmJpd24d1p9SY9p6MtoLfQynktM41lyhPrAnqoGf4pP+A0p9PVUxd1Cr8yfo6BvISp4NZeQdWEFdSFvRLwe8us7F9/wSlIZmKmPHU6JMgAsX0DQU0qINwmR0QHXmIHGHv7Ccf3MdtVtf4Vr/91HeuhGHmhx0njG4ZhzGZ8McFMZmKuLvoTr6NqiAqG3j0Fa3Cl0WnKK0pJiyjtOJzd4v96MpTqPJLQJtbY7sLKt9u1HwF665vfjp/fbvk16RzqifR1HYVEiKVwoPRz2Ms8rSGPTCi3FB4/i1SAgnjwgYQUBdABm1tqwKQ62BU2mnWHtJrqvyzalvaKdvR62+lhXXVpBZl0mCWwKTQyfjXOtMeno6Hs0eaBQa9CZLYh1JJOnp6RyvPM6HWR9iMBlQoGBm5Ey6enbFS+NFpV40b2JcYvCr9aOovohp4dNYlrsMnVFHjEsMQ5yHSJ/T+WHzoXUKbevprXx2+TNMmFCg4JGoR3BWOFODPEHfkbYDbKV5/kf4lyTodXV1uLpaUiqVSkVLSwtqtZrKykqOHj3KL7/8grOzM/feey9dunQhKkpUK7766is+/NBWkMoeHB0dSbCaozKj+XANIF+46tGSY/CiU6gHIZ5OJCRAW4JvQgK4+eSw9uQ1fF0dmDM0nk6hlgr5Rx4hPL7yFJdL64nydeGTSV1JCPHgra3pfHsgF6MJekR6sXRaD9y0GhKAyYVKfjoqxAL83Rx59vbuRPiIjnzbMz93rZoPd2RQWqfjzq6hPNgvior6ZjLK5B6op0v0JCQk8FlQJM+uTeVY6wz6u+OTpDmhzxLguapG6ppaaBdo3+7CVFADFMi2NbQo7F7Tm6LqKfjddjbbr8tI/MI6wCaVTLxC6+ohf532H0Bcsui4B3fFM/lBPM3VLuVc4Xtufn7MUGJSRsCOl+HQJwD4XFwhlNqtxZmy94hOVeQAUdUE2PCH7PzcCw7g3r49ZMs9KpVGPXGqAgi3ndn0dHXCMyEBagqEdUfOH8JabdxiMX9q0MO66a0dMQV0niQEnAyDIN2W+qPuMf2vXfNj1/9VfFC8dMxVlbZeykpfJQGBAUzaMkmymmjv3Z7lo5fjaKfYADDSMJILJy2MiwDnAEZ0H4FKoeLE8ROyfY9XH7f7nrzDvVl+bbkkEqVSqJjUbRIJvmJfo8lIfl0+gc6BNtVOg9HAubJzeGu9CXcX7InsNirGFfoKtEFaBnoNZK56Lp+f/pxmo22hTMH1P+f/24n7v2u99NxfBeVyFWo3J0c+OFbHroslBLlrWXBHR4a0t8yOXyyqQd9ikq2LAP5hOtZcOCCpBHu7ODBzRFcCrJhLfhf0kC0vCsbHRJHQRrjtrfHezF51moZmA+5aNW9O6EZCjNwa04xNZwv4/uAVNColswbHMrBV9f2Ztbaq3ACdYkJJSLAV0ZJDSVvbyODEgQTHtl47kwlUGrCqA2sctOLarn0PWi1iFCYDAWlfEzB8nvA0vx5MJtEpt6I6qr3DW/9WCdBjJOQdQ+HXjkDPcGx7Y4j58iubxdk3FBF65FWYNxESnoNbReExCAhq0cOv8qcq4Kbrj6pCBbaj04DQlhjWdRirLq6SzQkaTAauqq8yMG4gj6x7hIom8VlbV7COqOAopnaYisFo4OVDL7Pl8hYUKJgQP4EXe75IgiKB6dgK2q2JXsOaS2toNjQzPn480R6CUnww/yCfnPqEKl0Vd8TewSOdH0GhULBg6wLSa8X3ObUmlRVlK/hy2Jesr15/w/cL4OLgctPrUti1K1UZl2TbtF260JydjbG2Fqdu3Qh96SXU3t40nDpFw8mTOHXujEuK3HFFX1yCUuso0c5Nzz5LsUpFzdbf0AQFEfDsMzh36ULhunXIzJQMBoJravAYaGEOXImNpemCZZ12jI0lus37KH73PSo2bBAPcnJwrCgner24JqbmZq7NnUfdrl1233Nkj2Sc2hzvv2XNtOCyzRZXJy1T112jpqmFQHct305N5viAzmSX1hEf4IZWo2LIB3tpbBFJT1OLiaVnatn/zGDubP3zbTyTz+zfsmg2GHmwbxTfTu/PV/uyOV9QQ784XyYmh7H+dD65rfPn9w314Ke0A1xupcW7OKiYNbILMX6u/NSuPfszSymp1TEsIYDcigYK63X0ae/L2+M9eXFDGnW6FmL8XHhzYgoezhrmrT7D7owSgj2cWDCuI4Pb+3Mou4wlJy1z8l8fL2dYt3hajEZqdFdl1+BClZL54zoy8rxOmr+3h78U7ySOh/OWuWIc3Ajsdx+kycdFPON7i/jMK1xmmav0bF1Tf38RDrcWuZQamLwK+grHAukTVZgIV+ULZViHnhDc5rxrCsDpFhj9HKgc8FEq8SnPFuLDZZfE3Pq4xRARbzMX7qY2Wl2HZCjNgHUvSvsFH3+T4A69hYZTdZbsuX5lR/FL+ADO9pL7sPvEoZ38M3zWDamTrtDgcd8PeNhjWd0MdmLM6LhoHl3/qLSeH644THRANM+liPvM8aLjpJamMr7LeF7yEexLc0yZQAL7G/ZL44uhrqHM7j8bZ7UzytNK2f3DxUmsv4/vepx9pUI/I68xD5WzincHvivt97nf53xx5gtqmmuYEDeB+xOF49PzvzwvHc+EiZ8Lf2bWgFlsbr+Z3bm7cVI7MSR8CA4qB6p11UyKmMRDfR6isqmSULdQ9AY9qzNWCzejoJ7cFn0bCoWCuevmSoULEybWFK0hzD2MolL5571jVEcSwv+5GPNfkqC7urpSX29JkI1GI2q1eClPT086deqEX2u1Njk5mfT0dKKiosjKysLd3V02S/RXoLPD3dyTUcqejFJUSgXvjk9ifHf7XuT39Yrgvl72X79DsDu75g2kor4ZbxcHFAoFafnVfL3fslgfv1rJssM5PDZYBIFv3dGJySnhlNQ20TvaV6IZWc9cgvAfv2/JUapbKU1p+RfwcNJwe5dg/N0cKbGyxzB7ZoZ4OrF8uh1vxVaEtPHFzCiq5at92dTpWpjUM5wcO/NPDX/VB73nI0IN/eiXQrhN7Qh9nxTz5iBElMxewUq1Rf3cDIVCUN6tae9mtBsFD+2A9M1CJK7zJEEzOiZXdeTIl5YEffebsN/yJWbs59BtihD7KLISffCOFq8dYEfQISARAjsJsZFrrSuVUgM9Zoiftz0nknMQN4K1Dwka1PkNFroqJkHt7zBW2MklH4AT34ntKCDpbhj2yvWv619ArGcs93ew2NINCRvC+kxLQOqicaFXYC82ZG2Q+UBerLjIrpxdjI4eLW27XH0ZH60PHo4e3N/hfppamties50Q1xCe7PYkGqVIokNcQ8irzZOeF+4mEmidQceunF00tDRwS8QtBLgEsHTEUr4//z26Fh33tL9Hsk/KqMhgzp455Nfl4631ZuGAhZJVWlljGQ/9/pA0Gz+5/WSe7/k8HXw6yKiv7g7uhLqJ7/YDHR9gfPx4hqwcgg45NVD9/yN9zH/XejmxRxhHrsgTdB9nB4vORnUTc1ae4eiLQ3FUq3hk+Ul2XBD0tJQob354IEVaz3xcHdnyRD/WnMjDaIIJ3UNlyTnAg/2i+DW1UPLcHRjvR2c7qurDOgRw5IWhZBbXkRDkhrODuvW6yNfM41crmL3ytPT4VO5xds4biFqlZM1JW9sTgLuSryPGZg2F0lYJt9rqeAoF9J8LO1+1bOs/T/xfIy94YmgW6r8uPsJlImuX8LWNHiQ/3uh3RbGvpVHMuY94y/J7rYdlHQVxjBNLhSVknyfEGtXWRcKgE84RsUPl2w9/dvP3bwdujvaLvLGesTyf8jxOaifC3Gyvbbh7OOkV6VIwZ8bB/INMTZzK9pztbMreJG1fnbGagaEDJeVegKqmKip1lUR5RBHsGiypxJtR3ljOnD1z0BnEd/yLs18Q6BLI2JixNgI/p4vF52V45HBWZ6yWAi21Qm2jxBvndXNBvbYz4QDaTp2I+P47DNU1aAJEC6Xix+UUv2mZqfR/aj4+06djbGoi/8m51O3di0KjwXv6Q/jPmYPCwYHAF14g8AW5eKY2sSOsseo0KRRoE+X3rsDXXiN/zhz0BQWog4Ls0trr2nTZdRfS0RcVoQkMpHrz5usm5wD6omKc/oQT4L8S/+4Y0x6ulDVgbkoX1TTx+uYL/PxIb5JCLWtcUbVc8dr6cUZRLU+uPiNNSLyy6f/j7rzjm6rb9//O6E733rsUSssotOwpe09RFBFFhooi+CjuCepXcaOiIoKCIFsUZAio7D3KaOmkpXvvJk1+f3yak54kyKPio/6u16sv7WlyMkjuc4/rvq4Uwr2ceGxwG+k201Yc4+dUsQP+2a+ZfHxXIpvn9mTDqVxqGnSM6xSIQgGFVQ34uthL7hqPfnOaLWdEbApwtWfDnB4ce3ogBZUNhHs5oVAoeGFbiqTSnldRz7xvTnP0qYEcz7RcFTueVcaEzkEWfu1RviI3ffv2jnQNy+HNHy9TZzZN/8Pci3Efg1sQpGwFn3bC99wjXOyC//ymiHlthpvEhYe9IXQ6GqvAzgWGvS50go5+bDqnXiu0P6Jukz9W8myhJ2QU/u16v1hxao3DH8KuZ0VBrfEVlHWftvDjU6a1pPoy4c3+2CWxctTaJi1xuvx8Gfstxd3S9wo7NnMavlEYbtgbwnEo6xfwiRPaTJ4RIg89uVKIy3W51/oK1H8BW6WtbMChVqjJrcm1iOdGS83l55bz/mnTNeaZ5Gdk64vN+maW9lvKueJzVDVVkeyXLA1ipradKvMunxE/A71Bzy95v8gea3/ufkA4ehzIPUBxXTFv9X1L8io3orJJzuqr1lbTbGjG1c6VcdHjpONvHn+Try99jc6gY1DoIF7vLfRcnj/0PN9liKb3tvRt5NfkM6vDLMlByIiaphraerbldPFp2fHWTkJ/FH9Jltq5c2f27dvH8OHDOXPmDDGtPDnj4uJITU2lrKwMFxcXzp49y+TJgsZ66NAh+vT5c75xAN0jPCUvSHM06w0s2XH5hgX6zaBQKPDUmCaMRg/K1jAX/hA+6+JCXt2g5fFvz7HrYgFB7o68MrY9fWK8OZFdLhXnRuy5VMiExCBen5jAwvVnKa1tIsLbiWdHWu533AwVdU1M+viQJJC3+1IhL4yyPI+j3R/8SCiVMOhF8WMwiJ/WNLjBL0P0YCESF9kfvKJFkZ3xk0hqY4aCphUfpCpfKGr6xomgF9RFvu9jnGK1fsuMQj+6JlOH1Ihf3xYF+oi3TH6Wjp5CbAOg0zQhRndhowhqfRaa9synbRG7TFX5wgfdr8WP97r8C0lljrCUK7ViZVeaDm2U4vEGvSQ84m0ttQL+LCbHTObpbk/L6KN9g/vyaq9X2Zi6EWdbZ2YlzMLN3s3CHgOQktziumLm7JnDlfIr2CptmZ84n7va3cWcjnOY03GOxf2e6fYMjx94nKqmKjzsPXgy6Um0zVru/uFuLpWJbuGHZz5k7Yi1tPdqz5t937Q4x5JjS8irEVTasoYyXjj0AjvG70ChULDq4iqZcN2ay2uYGDOReZ3mcb3mOgevH8TfyZ9nuz0rs1dysXXB3dGdgjp5d9PVXCX1b8TfFS/HdQrk+/P57G0pyOMDXVGbqZxXN+rIKK7lekW9VJwDHMssY/PpPO5MNulA+LrYy1wvGrTN7LlUSLPewKB2vsT4OrN3YV92XyzEW2PHgNgb879c7G0kYbq6Jh1PbjzP9+fz8XOx54XRcQxq5ys9byO0zQYOpBbTK8rLYv9cAfzfpATJdeM3Ye8iV+sFS3HHXvMhuJsQiQvpYWLotJ8gFISlN6W92CG8sgPW3WXaBez2oEgwjWg7ChZcgqLLouC2ky/1SLh2HL6eaGogXNkh9iJDusGVVhMftYOl+OWp1SZF+Nb4LwSElFbSai97LzaP2Sz93j+4P8PCh0l7gYNCBzEodBDVTdUWiV6Mu/iMt/5OG5FRmSEV6CsurOCD0x+g1WuJ84xj2W3L8LCXC6eeKTojxS0jjuQfYVz0ONp7tudCqYn+bEyauvp15b0B77H+yno0NhomxExg7p65sudo3giwBn2tZZPbUF+H0t4epb0pDpUulzO0Sj79DM/776di3TqpWDZotZR+9DEugwZh3876Nd5t4gQaLl6kYvNmVI6OeD/6CHYRcns+h/j2RO7eha6gALWfn0xB3gjbiHCaMkzvvcrNDVULDbwpK9vi9q2hcv+TVoW3AH93jqlWWmp4mNt7Z5ZafjZGdwjgm+OmRvbIDibB2cPpJRZx62B6CX1aWEGZJbVScQ4iBVp9OJshcX7c1yucRl0zD359ij2XilAohNbSq2PbcyGvSirOQTReP/81k2dHtpO5Ap3NlXEzqG7QkVVSJ8Xh1ugc4o6fqz3PjWzH6zsvU9fUTHIrv3Z7GxUzeoWz/Vwep3LMbAf/qM6R2ta653jf/4iCWlsPzq2KtKiBojAuuiRySDuNUDPXmxXB2haKfLNONDVdA8Ue++xfhJaIg7uIp2WZoiEAUF8Be140FdQ1hULb6PavxCS8NWqLRWNgyhqhcVSaLuJ9QCcxrHFwE+LDPla+8z5xohE74i2xU99UIxyHBrasWLoGClE8fbPQazLCPfSWDH6e7f4szx96Hr1BjxIl/+n6H0JcQvCw95AV6UaRN6MosBGfXfiM22Nvp6yhjKd+fYpDeYcIcw3jpR4v0SvQtGLS1NzEvM7z6BHQg9TyVLoHdJeuEaEuoWRWmhgcRtbUI/se4edcYan39sm3+XLYl8R6mOyyx0WN49PzpiHe6MjR0kDJiNNFp2WCdruzd9M9oDsjI0bKtJIANqVtYlaHWUyMmSh7nRNjJlJcbyn+nFKaQp+gf6AP+qBBgzh48CBTpkzBYDCwePFivvjiC0JCQhg4cCALFizg/vsFfW3o0KFScM3MzKRnT+uiMr8HNxN3qarX0qw3oFTA9+fzOZFVTmKoOyMT/KX7Hk4v5VB6CXEBrgyJ873hOXtGeuFsr5appg+Lt0pCBODt3WnsbFH3zCmr46E1pzj61G1EeDmhUMj1ZYyT8v5tfDi8aCCFVQ0EuTvc9PUt3Z3KV0eycbBR8digGCYkBnEgtVimXm8wQHpxLaEeDmSXmXZ4nv8Dxb8FFArxY47w3uLHiA33mibNts4wY4dITq/uEf6QRlX3QS9Dz3nycykU0Oc/sMuoEKwQfuWtX2BrGBPa4CSYnyIsijwixKQfRPCf8JkIhEobefFs62Sy8WiNsN5w5mvT796xosnQZpiY3hsfU2kDMUNMt7vBNOr3wsXGhSqtfO/l9tjbUSqU6PQ6rpRfIUgThKudK6MjR1t4kI+OHM3qlNXS/qWPow8DQ8XE7ZNzn3ClXFxomvRNvHXiLYaGD72h8EWPgB7snbSX7Kpswl3DsVXZsjdnr1Scg5iCb0rbxNyOc62eo3UQBsiryaOhuQEHtQOFtZaWWYV1hUS7R/PxoI9p0DVgp7KTvhtp5WlUNFbQyacTI8JH8HkrfQOAwaGWlkV/F/6ueKlQKHhzYge+PJxFvbaZ6T3C+OpINqdzTImal8aWaF+NhcUZQEHlja236puaGbfsIJcLxGcr1NORrQ/2xMfZnqnJ1qdXueV1fHUkh0ZdM1O6hkirOR/tT2fbWZFg5lXUM2/taY48NZBIb0vbnyhvDRHeGvrEeMuTWeC7s/lMTPwvJuixo+C02SqKFZVvQruLn9ZIminUdS99J5K53gtErPplqVyo59hykVi2nmw4uJvO11Qn/HkvbRN2jcPfEGrxKZvl0/3GKuEw0W0OVGQL/12Nj7DccTRzADG39DHC6eaLcr5OvoQ4h5BTbfJ2HRI2RHYblVLFG33e4NHOj6I36CUmi7u9O891f47Xj79OdVM1yf7JzEwQLKTegb355Own0iRbpVBJiVt+TT7vnnpX8rRNKU3h8/Of83hXubhXjEcMSoVS5n3b1kNQC1/t/SrPHnyW88XnSfRN5PkepqS1X3A/SUhTp9fhbudOYb2IM/YqewI0N1f7v1EhbdDrZXZnFpoIevFcG9MtGxSN6RnYt2uHoamJulOnUHt7YxcpCh+FWo3/Sy/i9+wzoFJZtVQDUKhU2AQKGzmDTkfVzh9pykhH07cvDh064Pv44zRlZNKUkYHS1RW/l19C2aLa7jxwAKWffWZVZVHl5mZBz/878HfnmKEeDqSXyONffKAL5/NM1+Mhcb40aJvZfDqPwqoGRib48+KYOALcHDieVUbnEHfm9IuUbh8XaNk0jgswHbNTKy1yRHsb07//xpN5EvvJYIA1R3MYEe8vm3AbUV5rufbVLcLTLPbbEeWjwVat5PEhbfjkQDoKhYI5/SLpHulJUVUDXcM8OPbUQKobdfi7OnC5oIoZK49zKb+aPjFeJIV5WhTo7a28zj8Nexe5+JsRdhqTxS2I2Bg3DlJarbgkzxLF9+qxQqFdqRYFcM9HIDBRxOETK0TcjbpNFOF1pWJi3xpG9lTMEPmU3r+jqXHQs6XpV5YJH3Q1NYKDu8G9PwjW6ZGPxLWi453Qfrz4e4fbIXa4eAzPaEsNCOUfbHrcBGOjxjI4ZDDLzi6jvKEcBxsH1Eo1S/st5ZUjr5BTlcPAkIE81OkhwDLOGWPyWyfe4mCecEzIrMxk4YGF/DjhR0AMZjalbcJWZcushFnc215ub/x89+d5/MDjFNcX4+fkxzPdnuFS6SWpOAeo09Wx+uJqmfL7w50eJtg5mBOFJ4jzjGN89Hh2Zu4krSKN3oG96ejTkfQKy2FaRkUGaqUaRxtH2bTcxU58vh7t/CgRrhGcLjpNgncCY6PG8t6p92TnUKAg3CX8973ZVvBfFeg1NTXk5uYSEhKCo+PNp35KpZKXXpJ3uSIjTYFoxIgRjBgxwuJ+zz9/a6i+EVYSt9aYkBiISqngrV1XeP8nsdux8lAWl/Kr+M/QWFYfyebZLaau+329wqWpdcr1Sr49kYuTnYq7uoXi7+rA2pnd+OCnq1TUNzGlawgDYn0xGAwczSyjqLqRfm28cbEXnZsz1+SJblWDjoySGuICXHlyaCxLd6fSqNOTHO4hWXYA2KqVFurs+68UseV0Ht7OdtzfOwJfF3u2n7vOe3tNCpoLN5ylY4gbfmZ0UwB/VwcO/GcAKw9mcrmgmnt7ht9wX/2Wo+hSKxo4gr5z6ANhIbFvcSvLNeDA65D0gNxSDaDHQxDSXVhlhPYwTbbVtiLoHnyn1W1NNjSobUVH1Rp+z2R1yKuCzn91j2gsDG+ZCgd0hClrhVqyQiWaC15/zH/4t6A3p+EiEsv0inRm75lNQW0Bdio7nk5+WkbpMSJQE8j6UevZmr4VW6Ut46LHSV7lOVU5stvqDDryavKkAj1AKu3NAAAgAElEQVS1PJX3T71Pfm0+Q8OHMqP9DOzV9rTxMNHxWitiGtHU8u+qbdZy8PpB1Eo13f27CwXmoL5svmqayCX7JUv2a8PDh8s6mj4OPnTxNTEqWk/Nnz/0vETpD3UJtarQbO5leavxe2Lm3xUvaxp1jP7wV661NOjWHb/GxjndKa/TsvNCAcEejjw3sh12ahWD2vny2o7LktqwjUrBsHgx/fnu7HV2XSwkzNOR+3tFCIu28/lScQ5C1X3TqTzJlsccFXVNjP3wICU14vPxzbFrbJ/Xi0hvjSxpBKjXNpNaWM3YToHsTy3mh/P5KBUK7koOoUeU+HwuvzuRUR/8Slqhaef9QGoxF/Iqb54g9lkAF741WdzYaCCuRU32wkaTpU3idMHiMUfiPeKnNZrNEmJDs6nQLrkqJvHBSaJpCCLmGV0vCs+L6fv8i9b9zl0DxfMY8Zb4uRG8osBCo0chn+TfAFq9loJaOQvF+B3KqswipTSFTj6dCNAEWC1sx0SNYVj4MGq1tbjbmyZyCd4JvNH3DVanrEalVDGj/Qwi3cRnP7cm1yLGmcclEKrCTyc/zbun3qVWW8uQsCHc2VasSkW4RvD18K8t7mOONZfWSMU5QENzA68cfYV3+r/zG/cChZ0lI6Nyw0aUtrb4PWcSEvWcMYOiN0wrVx73TgdA078fFevXm85nb49T925o8/PJvnsa2lyxWuF+5534Pfes6XY2lp+7si+/pGLrVtReXng/PA+HeHE9vP7UU1RtE1TNko8+JvDtpbgMHUrE99vR5uai9vaWTfsdOnYk8L13Kf/qa5qystAVtlJdrqlBW1SEjc/Nmzq/B/+2HLO4xvLadnvXYOKDqrmQV0mPSC8evS2auz8/yvEskfMt25fO2ge6MW9gNHq9gY2ncnlhWwqdQ9yZmBhE1zAPHhsUw8cH0tE1G7gzOYSR8aYJe4CbA7d3CZYm8PY2SlmBn21lYp9ZUsvkLsEEezhIcV6pECtI5nhkYDQVdVp2Xsgn1NOJ50a1Q28wsPNCPnEBLpx+bjCqlhWjt3en8sG+qzTrDcT6ObPqviQMBgNzvxYaTQA/phQSZSUXr7+JgNxfjvHLhXp78WVRTIf3gc1zTPZqeh3sfRk63Cmo6q190q/uEdZrSTPFBLw1izJ+kvjvbS+KIv/qXvBtJ343x/HP5Cyta0cE/X3Qi4LSbmi2zEXtnMG7Df9rvH/mfb669BUA2zK2kVmZyfzE+TL2lBHT4qbx8VlTc+LeOFFsm68aFdYVUlRXxInCE5Jyu1avZenJpST5JxHnGYdOr+PQ9UMAbB+/ndK6UgI0AaiUKovzGe9vfKxlZ5ZR1lDG2KixUtH+3MHnpPxy+bnlLOm9hB4BPSzU2/sF98NGacO8TvNYfHQxBgzYKm15uJOoIRQKBWOixjAmagwgroPG1wCiyfxwp4cJdvkvhgE3wU0L9J07d/Lxxx/T3NzM0KFDUSgUzJ1rfQL2T8Gw9v6M61TEljN5KIAJnQPpGOwuiakZd8y/PJQlu9+qw9n8Z2gsn/8i72qvPpLN40PakFlSy7hlh2hq4TZtOJnL3gX9aB/oysd3J8ru89Da05KXr4eTLRtmdyfCW0NSuCenWiWcnk620qR8Vt9I7kgOoapeS5D7b1+k9l0u4t6Vpr3DXRcL2fNYX8kX3QiDAU5klXF71xDGdw5k0ylBIU4IcmVqN0FPnd4z3GK/8y+H1sr0zZgQN1RaHm9uFAV6s07Q042JalCiiV7aGoNeFMV7/hkRgEMtfXp/Ny5uhVOrwN5N7Jz6xt3YWq3NUPHzF8Ka/+KG1A1kVWVJyXRjcyOvH3+dIWFDcLRxRNus5XjBcdzs3Wjn2Y4g5yAe7PigxXkGhAyQWR35OflJthraZi2zd8+WaD1Xyq/goHZgatupsnP0DepLsHOwtJuusdEwPno81U3V3P3D3aRXiu5lvFc8K4eu5MmkJ3FQO3Cs4BjtPNvJrOD6BvflnX7vsCV9C572ntzX/j5ZUW7ExdKLsn377Kps1ArLMGd87L8C/5aYuSulQEraACrqtGw/W8DicfEsHie3ffJ3dWDdA9357NcMdM0GpnUPpa2/C98cy+HJTSb1sINXS9g0tycNOjMaIUjH1p+4xrt70mjQNnNPjzDmDYxm18VCqTgHUYRvPpXHwiFtSAr34NerJrVsZzs1bf1dsFEp+fDOzhRVNaBSyleP7G1U1DVaPof/qkB3D4X798DxzwX9u+v9Qqjnl6VyinjeKRjXIlCkaxJ6FPZuENjZ8pzd5sLmB0y/J9wuJtynVsG2eYBBPNa45ZAwSdhAtkZdKZRcEYV/ymZR0INICsOtWIvpmgSdMv+s+HuHKWI6k3NE+AKrbMVa0YBnwdtc190SV8uvWogtppansv7Kel458goGDKgVat7o+waDQsW+fHVTNQevH8TP0Y+OPh2xVdliq7JkIgwNG8rQMMtYmeCdgJeDFyX1pn/7ASEDrD6/yW0mMy56HDq9TmrqWUOzvpkPz3zIdxnf4e3gzfzE+XT168qv13+1uO3F0otWziCHrsC6EFb5txvwWbgQZUux6TnjXuzbxlJ38hQOHTqg6S1YAs79++P38ktUrFuP0lmD99y5qL28KHjlVak4Byhfswb3qXdKk3RzVGzaTOESYVvXCNSfPUf0T3vR19dT9V0rESmDgbKVX+LSEpdsg01JZHNVFSgUqJydcRk0CJdBg8iZMUNWoKPToSu8tQX6vyVetkaD1jK2/JhSyOr7TJpAZ65VSMU5QFOznlWHs0gMdef1nZf5pEW76Jvj10gtrOaZke2YNzCa2X0j0RsMVDfomLnqBIfSS4kPdGXx+HiWjI9ndIcAssvq6NfGG39X02d9UDtflv+SIU3YbdVK+rXxxlatZMPsHnxxMIvSmkbGdw6ieyvRzZzSOlYdzqJB18zU5BCWjBexv7SmkcFv/yytcSaFebBmZjLXKxp476c06XEuF1Sz/EAGs/pGSsW5EdaaBq2t5/4WqGzEumNrVF6T/67XQnW+YFqaw+htfue38OtSoT/UZjh0nCqKckdPMbwZ0jLNLU6FTQ8IqnuHO0V8b7Zs8EjHbrTe9DfBXF3929RvmZ84n6qmKpYcXcKR/CO09WjLouRFPNjxQRK8ErhQcoEufl3o6ifYC4m+iWRVZUnnCNQE4uvkazXGXiy9SKRrJPfsvEf6e7R7NKuHiSYuiJyxo3dHyYlDrVRzR+wdVDVVMWv3LGq0ojF/6dgl3Ozc6BnYk63pW2WPs/riakZGjOTDgR/y2fnPqNfVc0fsHST7i+/wlNgpdA/ozuWyy3T26Yy3ozdFdUUcLzhOjHuMpFHy/qn3pccDsbdvbBD/Wdy0QF+5ciXr16/nvvvuY+7cuUyYMOEfHzxVSgU9o7w4d60ChQJ6RnkztlMgU83E3xxsVTLat1HwSGVWqKoUChQK2HgyVyrOAQqrGvnpchGjO8gnBinXK6XiHKCstonPfs1k8bh4HhkYTWlNIzsvFBDi6cgLo+OwU5voKS72NtK0HUSQfHLTeQ5eLaF9gCuLx7cnyseZDafkIkjZpXUczyqjU4gbqw7Ld8g6BouJxdLJHXmwfxS1jTriA11RKBTsv1LEs1svcL2igSFxvrwxsQOaP7qH/nsQ0En4kOe2NBkUKuja4hWbOB12PWO6bdx40U3M2C+8IGsKBJ389q/FVMiI6gKRIBsn7b9VJOedEj7G9eXQ6W7o1FJcVuTAlrlijzQoCcZ8IAQ50vbAepPwGul7hRDHP2iXGaCTdycOXj8oO1arraWisYIabQ3TdkyT9rxHRoxkSe8lVs9ze5vb0el17Mzaib+TP3M7zpX2d1JKUyx2bg5cO2BRoDuoHVg7Yi1br26lTlfHyIiRBDkHsebSGlmBfL7kPHuy9zA8YjiLki2dAIwYGDpQouCDoFMdKzhGQW0BvYN642HvIUvmjbBR2FgIQGls/rqL4L8lZprHOXHsxrePD3Ll3SnyveYNZmJsp3IqyCypZXh7f97Zkyapurs62DC2YyBXCqp5YuM5KblbujuVGF9nXOwtY46Lgzg2q28EhVUNbDt7nUA3B54d2U4Wo3yssIMAorydyDPzkYsLsEKDtAbfOBhppvR8cqX893PrxMS6qQZWDDElbnHjYdIX8tt2uF34oqftEuyduBbq4t6XkNR2DXr46SWRwAV1NQlTgogzntFi9WbmXjG5sXG6cXG97SHx/EDoZ1ReE5T6+3aJSZG92+8SDgrUBFocc7Fz4f3T70v0dJ1Bx/un32dQ6CAyKjK4Z+c9VDSKZvSYyDG80usVAHZk7uD90+9T01TDxJiJPNzpYdnaVp22DkcbR+xUdnw66FOWnV1GcV0xIyJGWDCBarW1NDU34W7vjo3SRrZjqNPryK7KJkATIBXt31z5RtpLLKgt4OGfHmbPxD2082jH4euHZecOdb65kJjay/rKj0KlArPdb6fu3XHqbukh7j5pEu6TJsmO6azYt+mKS7AND6fozbeoWLcOpYsLPo8+guuYMdTs2ye7rb6ykrpTp7FvHyfosM2mglKhln/XDHo9BS++RMWGDaBQ4H7HHfg+tQiFQoHK0+z1qdXYxd7aKd6/JV62hr2NiqZm+TWl0GzlR20lvqpbqMlGZx8j1hzL4ZkWlqatWtzm0W/OSKJtx1oEMX94pDc9orzIOprDQ2tOS25D7QJc6BLmwUdTO7PyUBb2Nirm9I2UBj2+LvbM7R/JT5eKaNA106w3oFIqKK9tYuyyg5S1UN6/PZHL9od7Ee3rzDfHr8k0lo5llbH/SjGOdiqL7Ydr5XV4aWwJ9XQku9R0nwA3R7LNdJr83azH678VceOE0JoRXjFCP8TJS+yft7Zyi21hZmi8YWhL/lSVDx92NU3h4yfDhE/FqtLKEWKoBJD+k8hPE6fD6dWmoZR3LET0/ytf4R+GxlZDQ71Jr8jIsnzt6GtszxDNv1/yfqH0QCnrRq6jd1Bvegb2JKsyi1ptLU42TjzW5TFqtbX8nPszUW5RPNPtGZQKJUl+SdJ0HoQ4XFffruzK3iUr3tPK0/g+43smtxFaEgqFgk8GfcL2jO0U1RUxJGwI0e7R7MvZJyuWQYjK9QrqhRIl+lbuLMbBTfeA7nQPkMdlg8FAva6eUJdQQl3EdeBg3kHm/TRPalQ/lvgY97a/1yIXrm+up05b95uN4v8WN1WHUalU2NoKxXKFQoGDw59/0L8aJ7PLWfjtWdJLarlaXMv89We4kGfp0zv/thjZqvT820Sy82D/KNnxmb3DsVOrcHGwpJU5W0ksaxp0NzzmYKvi/yZ14PyLQ/h+Xm+6hok9wXO5FfySVoy2WU7/eW5bCrsvFlLX1MyxrDIeWiMoNV5OlpMIL40dYzsGMrN3OPY2SjycbHl5bHsZbT3SW0NCkBsKhYK6Jh0Prz3NtbJ6mvUGfjhfwLt7Ui3O+5dAoYC7t8DQ16HHPDGxiugn/tbjYZi0UhTOw94QypR6PWx5UBTnIOhJO1t2zmtL4PMh8FYbeCsGzqz97ceur4BVY+HKD6IQ3zoXLrcILG17WARqvQ5yDsGWFkG0i2Z0nvpyyDjA3wlrk+EmfZPFfnV7z/YEaAL46uJXUnEOsD1jOyklQgF9Z9ZORm0excD1A/nk7CcoFAruancXXw3/iv/r+3+Eu5qoycHOwaiV8seOcBPU3KbmJn7K+Yn91/aj1WuF+nvcNGZ3mC3tpJqrYN7o2M2w6NdF3L/rfp45+AwjNo3gStkVkvyS8HE0TXcUKKTdodawtbZTfIvwb4mZNlaq8Qt5lVw3N0f/DXhq5O+jjUqBq4MN7k62fPdQLx4bFMO8gdFsf7iXtHtpntwdzypjYFtfuoaZqM8RXk5MblFct1OreHVcPOdfGMLOR/vQM8p6UWSOGLN1HQUQ7v0nGjPmO462TmIac/QTU3EOYr/xmhV/mtDuQrgnYTKo1ILe1ChPJmhs+R70exLajRGNS/dwEQ9b62IEdLpxcd5UB+flUw9OrTb9v3vY71b1dbFzYULUBOl3BQoWJS2iViufkBm/x1+kfCEV5wBb07eSWZlJbnUui35ZxLXqa5Q3lvPp+U+lJC+lNIUxW8aQvCaZKduncK3qGlHuUSztt5TVw1czJXaK7LGWnVlGn2/60HddXxYeWIi21VTqStkVhm8aztitYxn47UD2X9sPCPG41qjV1nKu5BxTYqdYODvM62yme2IFTXnW/eE9Z9yL0gr9/b+F62i5XohNUBCOiZ2p3LKVshUr0NfWosvP5/qip2jKycE2ymyyrlRiGx6O2t0dt0kTTcfVajzuv0920+rde6hYt04U8Tod5atXU/uLKFYar8rtndDpaLxs6WX8Z/BviZet0ay3pGk72Mo/P+0DXSXbRwBHWxX39gwDsMglWw9ljDiWJWdDXsyvorZRx9YzeTy1+Twns8v5MaWQqZ8doa5J5JdD2/vzfxM78Nr4BJIjTFPya2V1DHjzAI+uO8O9Xxxn+hfHMBgM7L5UKBXnIByQtpwRn+lqK3lsdaOWxFB3fF3kn+3h8UK/6b0pnYj1cxZGF9FeDIvztTiHzU00lP40Ci7A54NhSTB8e6/I926GrvfBqHdFkZw4He7eLBpbLgFCJDh6iNAcmrRSrDed3yDyP+PF7OhHpuIc4Px6sXqZfchUnBuRsllQ32f/Cn2fhKGvwYwfreuc/APwSOdHJNFhI30bLGPpxdKLVDVVca3qGmO2jGHM1jH0X9+fbenbcLF14f/6/h9Hpx7l6xFf09ZTrJj2D+nPgsQFBDgFEOkayet9XifMNeyGOaLeoOfdU+/Sb10/pv4wFT8nPx7q9JA0zW6dpxoR4RqBi62L7PqhUqiYET/D6us9XXSakZtHkrwmmWk7pkls1GVnlslYZB+d/YgGXQOjIkfJ7t8zoCeeDr9hq/o7cNNRaWJiIgsWLKCwsJDnnnuO+Pj4m93lb8evafLus8EAv6SVWFAbpySF0DnUnZPZ5XQKcSPWTyRg4zsHEePrzOH0UuICXegRKRLCO5ND+PbkNYkW2j3Ckz7R3pijS5gHbXyduVIoPmRKBUxJuvE+woNfn+L782LiHu7lxLezu+PVQtc0p6xfLqimsl7LzD4R7L5YyPUWm447koKJ8RUJ6dMj2vHU8LY3FZO7WlRjEYTPXPsvgtmtgp0Gus22/re4ceLHiPpyqDKzTipqESDbv0Ts8ICgx2+fLybnDu4iiB5ZJv7WbS5E9BVe641mDZtL20VnNOeo/Pi1o+ID5GZlmuIWYnnsb4a7nTuDEgZhp7LjQO4BIlwjJNX10oZSi9uXNpSSU5XDkz8/KXlGfnDmA0JdQ63STgE8HTxZlLSIN0+8Sb2ungTvBB5IeIBabS13/XAXVytEUhfrEcuqYassOonDw4ez4sIK6nSie+xm58ZtobdZPA6IoL/k6BKyqrLoF9yPRUmLcLRxJKMyg+8zTKrVNdoaVqasZEnvJawatoqVF1ZS3ljO2KixvHn8Tdl+KVhvbtwq/Fti5g/n8y2O7UwpZPelIpaMi2dyVxGz9lwsZNWRbBxslMzuG0mnEFMhPW9gNEczy6ioE8XRg/2j8GhpHvq52jNvoFx7oaMVW7WOwW7YqJR880B3Dl4toUHbTN823jJmkTnyK+v5ObWYCG+N1OQE+OZYDsv2p9OsN9BkRrM3ICj4Q+JuLOL5m+j/NKy7W9AfAfotEgW6ucI7WD9mDoVCJINHW/n4JrYI5Ni7wORVojF5AyEwGXSNQoxSqRTPyU4jXxX6gzY7rfFCzxcYGz2W9Ip0ugV0I1ATyOH8wzIK5MQYUQzeKMHKrsqWed0CnCw8yajIUTz1y1OSqntKaQovH3mZ5YOXW5wHIKUkhY/Omt63H7N+pKtvV8nS543jb5Bfmy897kuHX6J3YG/aebSTinUQ1Mhot2i8Hb35YtgXwv6xuZE7Yu8g3vvm31t9VZXFMU3//njPE8V9U24euqJCHBISLCbXvwXnAf0JWraMym3bUHt74znjXhQ2NtSfPmX2BPTUnz2L5733Un/yFHXHjqGwt8f7kUewDRKsB7/nn8d54EAa09PR9O5tQZNvTEvDHI1paTgmJdF4xbIYVzrfWp2af0u8bI1GnaXwWoSXEzsvFPD10Ww0dmrm9ovis3u6sPtiIYVVDQyO85OsbxcOiWHB+rPoDSI/XDjEkpXQKdhNmqADxPhqcLJTsytFfi0rr9NyLLOM7pGezP3qFHsvCxX3KV1DWDyuPQqFgi8PZVFSYxI1+yWthGOZZbhZGTq5OYj4PaFzICsPZdLQsjPu62LHbW19sVOrWDuzG+//dJXCqgbGdgpkTEfxWesQ7MbOR/tIK5NdXtltcf7Lhb+/Gf9fw2CA9XebGqYpm0QjdcwHv30/ELHY3PoMhEPG1BadiLJM4TFujO+xI2HK12JIZI7aEtEMNYcxb/SMhP43ZgxahdHC+H+IsVFj6ezTmfMl5+ng3UEatMR6xFKcZ7rOBWmCcLZx5qXDL0l09npdPYuPLua2kNtwtHFk+bnlrLuyDke1Iw92epChYUOZ3n4609tP51r1NUrqS9DpdQwOHSx5m4OwBh4WPoyNaRv57LzQBChtKGX+vvn8OPFHSRspzDWMRzo/wsdnP6axuZHu/t25u93daJu1LOiygN6BvblacZWegT2JdIvkeMFxcqtz6RXYC29Hb/QGPU/+/KRkP3y66DSvH3udt/u/LT0XIxqbG9HqtdzX/j7c7Nz4Ne9XotyimB43/Za99ze9YsycOZPTp0/Ttm1bIiIiGDDA+g7YPwlt/S0vIDG+Gt7YeblFzMiJRcNjifTWEOPrLBW2rdE+0NWioPfS2LF7fl/2XynGyU5Fz0gvq3vbKqWCdbO68dWRbIqqGxnTMYDEUJFEHs8qo7JOS69oL+xtVBzPKpOKcxCiHqsOZ/PYIDEd6RTsxq5W9kaR3k64Otjg6mDDTwv7cTSzDG+NHe3MqJuti/Pvz+Xz5aEs7FpERYwNh4LKBpQKaC3ymRRupvz7T4GDOwR2Me1egsm7suiy/La6ehFIbYvgqwmmhPrqXphz0OQh2RqeLeJMwV1FAW9EUJJIpJNmChXk66cAhRCtM/fF/B/DnLYNkFWVRVJAkhT0WmNM5Bi+S/9OoqT6OfmR7J/MjswdFknziYITDA0bilav5cC1A1Q1VTEgeABu9iLRn9xmMiMjRlLZWIm/RgjZrL+yXirOQfiq78raJYlpGBHsEsyaEWvYkLoBG6UNk9pMstpxbNY38+i+R6VEe8vVLWhsNDyR9AT1Osspr/FYoCaQp7s9LR3/9OynFrc10rT+CvxbYmbPSC+2n7Ms0oUV5SUmJAZx5lo5M1efkAYFP6eWcODxfhKtPC7AlV+fGMCxzFLCPJ1ktj3W0D7QlRdHx/HOnlQatHqm9QhlZIL4/KiUCslSCCCjuIYlOy6TUVzD4Dg/HhsUg41KyZGMUu5ZcYzGlnWjGT3DeW5UO85eq5Dtw1tDk7k30u9Bm2FirSX7oJii+LRYunSYIuiKRkEzZ38xiWmsEdY72QeFGvCAZy2V1YcsFuKWuceFXVvCZPnfWxfnZZlCUyO4G7i0iEc11cHWB4XgpqOXoFzGTxSP9cPjgAFUduL3W4COPh3p6GOKe08nP01bj7aklKbQxbcLIyNGAjAhegI/5fwkxZpYj1jiveJxsXVBgUI6DmKfsEHXYGG5drHsxjvgreOMEWkVpkIzu0q+5lVcX0ydro574u4htTyVvTl7cbVzZWGXhXg7ekuv7R2f3xaFM4euxLLpaSzEi997j5KPPgaDAZugIEJWrpSKZm1+PmVfrqK5ogLXceNwSrZURnce0B/nAXLaq0PHjlS09kFXKnHo0AGViwuhq75Em5+P0tkZlcb0PVQoFGh690bTuzfWoOndi5IPPzRNA5VKnHr2pDHtqqQ23xranGzswm6dj/i/JV62hou9mrI6+R6xj4sds786Kf3+S5qIlcPj/cmrqGfFr5lU1GmZ3CWIcZ2CSAzx4FROOR2D3QjzshRTe3lse2rXn+FIRhlxAS7830RhERjmJdcnUiggzNOJjSfzpILeYIC1x3IYmeBPzygv6qzszNdpmxkQ60O3CA/JljjKRyMxl6J9ndn6YC/WHb+Go62Kqd1C0NipOZJRSl2TjtcmxEtN1BNZZby1K5XyuiYmdQnmvl7hVNZrZboiRrg7/oWT4up8OZsJRPwFsRa09WEoSoHIgTD2I0FT/z048pG8+Xp5u5iUd5giVomM1wCXIKH9YWMvlNsPvS/+5hsP3R+68fmbauHcemHXGzfelKuWpMGmmeI1+HcQWiXG68//ACEuIYS4yAdSTyU/xcIDC0kpTSHYOZjFvRajUCgshDxrtbWU1JdwKe+SzCP9yZ+fJM4jjmCXYJaeXMrKCysxYCDEOYTPh3zO2hFrWX9lPXr0TIqZRIAmgOMFx2XnbtI3cbb4LANDBpJRmcGx/GMk+iayb/I+arW1+Dj68MbxN/j2yreSSrwxL37h0AtsTNsIiHXMzwd/ToAmQCrOjTBS7SfGTOTNEyZ74CGhQ3C2dZb+ZmxO30rctEB/4IEHWLt27S3xjvxfYVA7X6b3COPro9koFAqm9wjjbG4ly/aLvderRTWkFVWzb0G/3y2MZm+jYmj7m09g3BxtZZ7ABoOBmatOSDYYQe4ObJrTg9KaRov7tj724pg4qht0HM4oJdbPmTcndZA9l9YUKms4nlXGQ2tPmZg4GWXsXdAXL40dC789KyvOo300PDwg2vqJ/gmY/CX8+BQUnBcqnEZ1zOjbhECTEc7+QlX90Pum4hzE/1/+Xgi89V4AB98VVPbQnpA0S9xm9AetdtC7Cno9iAbBA/vEY9u7/iOn54BVESYjkvyTWD54OduubsPN3o1p7aZhp7IjzjPO4rZxnnHoDc5uMpMAACAASURBVHpm7prJyUKRdLxj9w5rRqyROqiONo442piSBfPdH/NjWr0Wg8GArcqWSLdInkgy2eLp9Dq+z/ie1PJUegT0oGdgT3JrcqXi3IhjBYI63M6jHQleCZwrEWqeSoWSyTFmxU0LrDEHrDU3bhX+LTFzSlIwW87kcdSMpQPCXULbrGfH+QIZJb1e28y+K0Xc3tX0+dfYqRkQa6IybjiZy7cnruGpseWh/tEWzcN7eoRxT48wDAbDDVk+er2BGSuPk9Wyz/jR/nTUSgULBrfhw31XpeIc4MvDWTzYP5IjGZb/zq2hUihk4kh/CK6BlkV0aA+Ytk1YLtq7Qfe5go6+ebZI2kCItVXlw53fyO+rVEKnu8SPEfpmofpu04p5cnIlfPcoYBCT8smrhO3OofdNlkG1RWIlJ7yPaChGDoCCc0Is0/kPsgZuArVSLe0FtkbvoN58PuRzdmTuwNfRlymxU1AoFIS5hvF89+d57/R71GprGR89nrFRY1EpVSR4J8jUeZP8RNF6ouAEi48tJrc6l0Ghg3im2zMk+ydbqO+29pwdEDKAtZfXys5lTKbe7v82ddo6bFW2Fqs6vxetGw3SMb0e7fXrlHz8iVT0anNzKf3kE/xffgl9YyNZU6eiuy5iW+W2bYSs/OK/si9zHTeOxrSrVKxfj9LZGe/5j2IbYvou2vibVL+r9+6l7thx7BPicRk+XPqula1ZQ8XatSgcHfF+8EE0ffoQ8MYblH3xBSiVeN5/P/axsVY93sVrvrUU5X9LvGyNntFefHdWfm0qNyvYaxp1/JxWzNA4fyYsO0RBlWA7bj6dy7pZ3eka5oHeYOCJjec4c62C5AhPXp8QLwm/Bbg58MX0JGyUCtRqU6NuZu8IDqWXcjqnAhuVgnkDognzcmLtMUuHg8ySWnpGeTGlazAbTuTS1LJCGeHlRM9IL9QqJWtnduNwRimNWj29or2wUSlZfSSbbWfy8Hd14JHboon01qDXG5i24hi/tDBUwzwd2TinByqlgntWHKO2xeHj5e0X8dLYWvVPB3hi6F9YWGp8wTVYLvoWmCi+h99ON9HQr+6GHxcJa11tPex+HtJ+BO+2QtzN2hAHTCtI5sci+omVzTNrhEhctzkmLaRBL4mBTl2paOwqFKLgPrtWKLN3mgZOnqIZtnKESRn+l6WC+u6fIFYvjcfzz4q1zJk//fn3608gyDmIb0Z+Q3VTNRobjRRf+of0l9nrRrlFEeISwqqLq2T3bzY0c7JI5JbG4hwgpzqHFRdWiAZA14Wy+8R5xrEj02QXqlKoaOfRjj3Ze1h4YKE0aJrTYQ5zO87l+4zv+fqScPFo0jfx1sm36OrXFXd7d5mYcL2uns8vfM47/d8hyi1K1gBO8hdx+Z64e/Bz8uPQ9UPEuMcwKUauG/JX4KZXJ1dXV7788kvCw8NRtnTze/XqdZN7/b1QKBS8MDqOx4e0QaEAR1s1oz+Qq7Rml9aRUVJDlI8z649fY/2Ja3g42TJvYLRscn65oIqS6iaSwj0k8Q4j8ivryS2vp0OQm8XfzHE0s0wqzkEoWa4+ks3svpH4ONtR1CKmpFIqGNfJJMjj7+rA2ge6/Vcq64fSS9hyOg8vjR3Te4bh42zPnouFsgS7qVnPgdRiOoe4ywTyADT2auxt/ho/xVsC1yCRmJqjxzwxSbq4RVDRb3tB0DzdrXT5jccGPgfJc4SPcOtg7B4K935veT8j/P459DtryeGaS2sYGz32hvfp5t+Nbv7dZMei3aN5MulJlp1ZRoOugXHR4xgdOZpjBcek4hygvLGcby5/YxE0jRgWNozPzn0m+aq72rlKXslfXPiCT859QlNzE+Ojx7MoaZGkyAnw0uGXJAuMVRdX8Wy3ZxkXNQ4Pew/KGkwFpLGZoFAoWD54ORtSN1BQW8DQ8KF08DY1r4w4dP0Q2dXZFsfL6i2L0luFf0vMVCgUrJvVncKqBj786SqrjpjepxHx/tjbqCysHQGCW4SH6pp0nM+tJMpHIymo77xQwMJvTRZ2h9JL+fWJAVaFJ40X9IvXq3h3byqlNU1MSAzijqQQMktrpeLciH1XilgwuA31TfJpULPeQKNOT3yQpWBjUpgHJ7LL8HSy5dlRcdLq0C1HeG/x0xqXzeJI6k7hQqH6jcvuyS9hz/PQUAXtJwhqplINe15EEpPTa+Gnl0WB3trmB0RhX5gi/H49I2+caP4P0NWvq6Ti2xoTYiYwIWYCeoNe2m0EeKPPG7x85GUullykq19Xnun2DA26Bubvny/ts29L34aXgxfzE+fzwYAP+Pjcx9Rqa7m9ze2yAn1BlwU4qh05kn+EWI9YaW/SiNaNxT+D5gpLbZvmqkp0RUUW02dtodhlrD18WCrOAdDrqdyyVSrQ689fQF9Xh2OXRCE21woKpRLfJ5/A98kn+C2ULP+U4qUmkcOG8xfwffIJqvfto/Cll6Xj1x58iKidO3AdNRLXUSNl51A6OYGNDWjlhae+5tZSlP8t8bI1zGMQgI2VVZRgd0d+SSuWinMQjMVNp3LpGubB/PVnJBvJn1OLWbTpPCvvTaKmUcdj686w+1Ihnk52vDC6HSMThBixm6Mtm+f2JLOkFlcHG2mlaHCcXMXdTq2kf6zQY0kIcmPLgz3ZfDoXN0db7kwKoUHXzOIfLnEqp1yyeLNRKVl//Form+FyjmeVceDx/hzOKJWKc4Cs0jrWHssh2tdZKs6N2He5iDEdA2XTeYB+bbwZaSaqfEuhVMGEz4VIZkmqKJyHLIaaIvmOOJh0Qva+BMc+Ef9fngXlmfBgy6pjzlE485UYyiTPhs7ThPimkXHoFQOhvaAyTzhjjH5fvkveWCOcMzwjxeQbBOPz0/4mgbiTX8Lcw4JF1Tqea+uEE8eod4WocWuY//43wtj4NOKB+AdQomT/tf2Eu4ZLHuk3GgQV1BVY5LKFtYUWtwW4M/ZOUstT+SHjB1zsXJifOB9/jT/z9s2TsUC/uPAFM9rPIKU0xeIcRraX+WMaGZhL+y3l1aOvklaeRo+AHjze9XHpNkPChkg57f8CNy3Q3d3duXz5Mpcvm2jE//TgaYRTq6QwykfDuVzTxVRjpybAzYEfzufzn42mrv3RzDJ+faI/zvY2PLHhHOtOiE5csIcD387qgZ+r6Ip9ciCdN368QrPegL+rPV/dn0zkb9A7K8y6qwDldU042anZOKcHKw5mUlmvZXKXYLqEWdLMjcV5k07PFwczpaB6T48wbFRKfk0r4e4VR6Xg/MP5fHY/1pdwK9SpCC8nonw0eGlsZRSkbhG3Rtjgfw6lCgY8LX5ao+0YsSN0ucVmJnakOGaExvv3U5z+QXC1daWySZ4gXq8R9By9Qc+G1A2cLDxJvFc8t8feLlM4NsfUtlOZ0mYKevTS7ZrMvZsRezcAZ4rO8OGZD6lorGBs1Fimtp2Kv8aftSPXsjF1IwqFgkkxk/By8OJi6UWWnjQli+uurCPBO4HRkUIIqaaphm3p22SPs+bSGia3mczrfV7nhUMvkFeTR8+AnjLrNScbJ+6JM/ObNsPe7L1WjxtprX8F/m0x09fFnhdGx9HG35lD6aW0D3CVxIwmdwnmx5QCDqWXolDApERh0XMyu5wZK49TWa/FVqXktQnxjO8cxM4L8qlSRZ2WI+ml3NbOl8PppXxzPAeNnZr7e0cQ7uVEbaOOqZ8dkaZPJ7LLcbJTc1tbH5zt1FQ3mpqIMT4iEbi7eygnsk32Rbe19SXAzYEANwceGxTDJwfS0Rtges8wnhgaS5NOj41KcVNNjlsOz6iWlZgWeISL4rwqH3Y/C/nnILK/aBbaOgkHie2PmmiS59eL6UnSLNFIbI26lqQ3vDekmiYK2Gqs27z9A9G6OAexmvLxbR/Ljl0svSgTmwM4VSje0x6BPegRaN0+005lx6OJj97CZ2sdtkFB1JkdswkMxD4+HtvQUJqyTU0v15GiAFZ7WF7f1R7uGAwGch9+mJo9ImbZRUcTunoVKrcb6wfo6+ooW/0VjVevounTG9dRQrCofPVq2e3Kv/kGn8cXUvuLmZ2cVkvtkaO4TRhv9fxqf390OfLJrE2QpYf2n8G/LV6CENttPXBRKmB6rzDSims4llmGQgF3JIXQJcyDk9mWzWBPJzt0zXqpODfieAubadm+q9JqY0lNIwvWn6VXlBdujracyinnqyPZ2KqU3NszXCrQE0M9+GhqIl8eysK+RS8k0M2B0ppGVhzMJL+ygVEdAujfRhTtc746yY4Loml0LreSkppG3p3SiR1mMTy/soGzuRVU1FnmA+V1WqJ9LPPe6Ja10eXTuvD5L5mkF9cwqJ2vtKv+lyIkGR46LmzLVC05j8EAHhFy+ntIi3L31T3y+xdfhoprwhVo5XDBsgQ4vxEePgH37RZFupO3EJc78qFooBqawTkA7tkGXtFw/QysHiv0k1AIgdBe8+Xq7SAaAmm7xOTdHIqWBl1YT6EAb0TYP/f7oVKqmNVhFrM6zJIdHx05mpTSFDalbcJB7cDcjnOJdo9Gq9cSqAmUCRgPjxhOcV0xb518i0ull0j2T+bRzo/iaOPIq71e5YXuL6BSqqRriDEvNUKn16E36EnyS2L1RVMsVCqUdPHtQoRbBEl+SRIjU4GC29sI/ZJw13A+G/zZX/Le/F7ctEBfsmQJqampXL16lfDwcNq2bfu/eF63HP8ZEsvVohrO5Vbi5mjDK2Pb42irZleK3Me0sl7LkYwyAt0cpOIc4FpZPZ/9ksEzI9tRWtPIm7tEcQ4igL27J4337hAWRD+cz+dYZhmdQ90ZlSDULfvGeOPvak9+i6ibWqlgQmdxoQv2cOT5UfLuUlltE18dyaa0ppExnQLp3CLM9Py2C6w9Jp7XjymFZJfW8fLY9nx78ppsUp5VWsexzDLGdQ7kp8tF7LpYiFIBU5ND6dGigrx8Whde3JZCdlkdQ+P8eGTgP5je/kegUgsBj9IWS6+/cZr0V+C13q8xZ+8c2bF2XsKq5d1T77LiwgoAfsj8gasVV3mhxwuACGZH84/iae9JnJfpc6dSqlBhmth09+9OhGuEtBtqr7JnYsxEKhsrmbV7liTy9tqx1/Cw92BY+DBCXUJ5rMtjsud0I69LY4GuUqpQK9U0t7ICsmsRQunm340d43eg1Wtl9P3C2kLc7d1lx9LK01hzeQ0Gg4Hb29xOW8+2BDtbF2ecnXADccJbgH9jzFQqFUxNDmVqsqUV5ZqZ3bhaVI29jUqy7XltxyUq60VR3dSs5+XtFxnVIYAQT8uGYKinI6dyyrnr86NSzNxxoYD9j/fjZHa5BTX0x5QCRncI4LUJCTy95TwVdVriA115fKgQUhrTMRBvjR27LxUS4a1hUqKpYJg3MJq5/cT3XN2iUn8zdtNfhuFvwrqpYi/S0RNGtuw3fzvdJGpZckUI/4x6RxTsBrOd3+tnxEQmYYqY5BjRucXyMWkWVF0Xe4vOfjD45X+c9aMRaeVpbL66GXuVPZPbTMbP6ea0+3DXcJxtnWWicwneCVZvW6etY1PaJgpqCxgcNviGt7uVsPG3fA02AQEoVCpCVn5B6aefor2ej8uIEdKE2iEhAZcRI6j6XjAsbAIDcb97GnVHjkjFOQihtvL13+L1wEwa09MpW70ag1aL+5QpOLQIqeU9toCa/fsBqPruO3SlpXhOn47CXm5lpbC1BYUCuzaWYmTWjukbGmiurML3if+Q96BpZ9aubVscE27t+/pvjJez+0ZyNLOMM9cqUCkVJIW5o1QoWD+rO2mF1TjaqSVBuMRQD0bE+0taQ6GejkzrEYpapSQhyFU2ODIKcJ43cx1q1OlJLazBzdGGKZ8ckajq35/LZ++CvpImSM8oT5LCPaSiXa83MPWzo1wuEN+fTafy+OTuRIbE+cm0jUDEXfH8nADTnrVKqSDI3YFYP2e8ne0k60xblZLxnQOJ8NawaFgsb7foivRv4829PcO4XFBFblk9ozr4s/l0HudyK4n1c5G5Cv2lULUaSCgUMOlL+O4RKLoodtCHLBZ/840T03YjHL0EVf7Xt03FOUD1dVEktx0FQYniWF0Z/PSKaaJefV2IFk9cIf5bb2wkG2DfEiECqrZiM6d2EKuWId3FeiWAnYugxgOM+VCsOOUcETpJI3+fVsY/ASqlime6PcMTSU+gRCkxKG2UNqwYsoLPz39OUV0RIyJHMCRsCNN3TpcYnBmVGTQ1N0k57K7sXdKE/u52dzM1diqvHH1FeqwxUWNwtHGkX3A/FiQuYM3lNdip7JjdYbbkOPTBwA/YlLZJWp3q7PvPa2zftEBfvXo127dvJyEhgRUrVjBs2DDuu+++m93tHwc/V3u2PdSL/Mp6PJxsJXGLUCsJZbiXI9crGiyOG1UwS2ub0DbL6RH5LR6Yb+9O5d29Qqxm5aEsUvIqWTS8LQ62KjbN7cGXh7KprNcyqUuQTA25NXTNeiZ/cpirRWJ/96ujOay5P5nkCE82npLbumw6lcvLY9tbFd5wc7TBTq1i+bQuXK+ox0alxNvZRPHsHOLO1of+uZ24W4b/zwpzI3oF9eK1Xq/xytFXqNHWEOkaKfmIb7m6RXbbrelbeabbMxTVFXHPznsk64hREaNY3Hux1fPbqGxYNWwVW65uobKxkpGRI4lwjWBvzl6pODfi59yfGRY+zOp5uvh2QalQom9VfBj3S0EIdNzb/l4+PiumZyqFilkJpu6rQqGQCvGC2gIe2fcIF0sv4mrnynPdnmNw2GAKagu4e8fdku3T9xnfs3H0Ria3mczPeT9L4iLGc/cL6ffbb+6fwP8vMbM1onzkSZV5fCyv01Kvbea+nuH8klbM6RyRuM7uG0G0rzPPb70gFecgGpD7rxQTH2hZTIa3xOQRCf7c1s6HstomaS/TiB5RXlKj0Rzq3zJz/18iKBEePS+mNu5hQn23vsJUnBuR+qP4b3CySN50rd7b8Bba9qh3xDT9+mlh99PxTnFcpRY7k0Ne/ctfzp9BRmUGd35/Jw3NLXu4Vzezbew2C3qkORzUDrzZ901ePfIqeTV5DAwZyNyO1j2yZ++ZzekiQRFdfWk1ywYuo2dgz1v7Qsxg42elQG/ZA7fx98fvuees3i/wrTfxmD6d5opynJKTUdjaUnfsqMXtdCXF6EpKyLrjTkkxvuq77YRv2ojK3V0qzo2o3LgJz+nT8XpwLvmLnpJ24L3mzkGhVOI2fhx1J05Q9f33KGxt8Zx5Pw7t5cOBik2bKVy8GH1NDXZt26LUaNDXiFzEsfOtT2L/jfHS3cmWr+9PZsCb+ymsbuRwRhmj3v+V71o8xM3x4dTOPHCtgop6Ld0jPKWm4dLJHViw/ixncytJCvNgyXjReOkR6SWjk7vYq2kf6MLH+9Ol4hygulHHjxcLubtbKG/+eIXlP2eg1esZEe/P0skduZhfJRXnRnx74hpD4vwI9XQko9ikMxDWEnfn9hfNh0v5VdiohO6HMf5untuDVYezqWnUMaVrMHEBIn7P6hvJXd1CqWtqxtvZjsU/XGL5z6Kxr1CY9Ae/PprN9od7E2Vl6v6Xwz9B6AiZY9DLJgFOja/QIVLbWgp6AjiYHaspEmtFrVHZkqPXmNG0mxuFs0aXGWKKbvx7cDJEDRRv1LStcOk7oQDfbrSweQPxX6OS/L8c1picAZoAnu1uEjKt1dbK1isBDuQKW+NvLn/Dq0dN17sThSdYMWQFwc7BHM4/TIx7DMPDh0t/tyaYDOLaMrXt1D/7cv5S3LRA3759O19//TVqtRqtVsuUKVP+8cHzt2Ce6M3oFc7BqyWcyC5HrVQwt18kUT7OBHs4EujmQF6LJ7BCAeNaJt7RPhpi/ZxlgW90C3Vn1eEs2flXH8nmyWGxKBQK/F0deHKYXCCjplHHd2evo20WQdVTY8exzDKpOAexY/n/2DvvsKiurQ+/U5ih996bSLVi75rYjWkaU9VYUkw3JjHVNE0+kxtzb3oxURNjLzFqYom9F1BRBKRIUTrSYZjy/XFghgHsIO28zzMP7DP7nNkHZtbstfdav7XyWBq9/B1wslTqxwToV06n9ffj75hMfa7TfV099MYTBMERkbbHmIAxjPQbSUFlgb7UBAj537Vzt20UNsgkMpadW6Z3zgE2JW3iibAnCLYP5sjlI/x85mcqNZU8GvIow32HY6O0qRdG7m/jX28ctY9dLrnM1pStWCmsGOM3Bl8bXz4d+CnfRX9Hubqch4MfZqi3sVLvrC6z6Ofej/iCeHq59cLHumGV4P+e/K9+R76wspB5B+cxwHMAOy7uMKrJXKGp4O+Uv5nZaSaLRywmpTAFcxNzoxrpTUVbs5kNMb6Lu150E2BosLO+lu/6Z/txIbsYazMTnK0E+1R7YbAGJ0slfo4WvHxXEF/tSqBKo6Obty0zBhjeS0q5rJ7NblXITMCp1g6l0hqsPaCo1kJrzfOWTvDQ77BzHpTmCcJxNTvlMhPoZRwy2JrYlLhJ75wD5JbnsittF/cE3EN0djTfn/6e0qpSJgRNqFdXtq97Xzbfv/maooKxebF65xyEFJ+VcSub3EG3GjkSs+V/UH5K0F1QduyIpqCA/GW/YTNurD48XafVUrJrF6rkZCwHDULZoQNmEeFG17IcNAiZnR2agupdN5kMm3HjKN6x06icm06lonDzZhynT0diZoau3DAfkNkJi/62996LWVgYZSdOYBoeoXfCJSYmeCz8P1zffguJiQlSc+NcfM2VK2S+/z66SmEzojI21uj5guXLcZj2JCbujZdH3Frt5Y7YLLKKDaG1ZSoNq4+n88aoYM6kFwq6GqUqJnT34pFe3nRuoMRkoLMVG5/rX++9PX2AH3kllfx56hJutma8NToEc4X8qnY0KrWAr3YZhK3+On2ZvgGO9AusHzZds5nz8b0RzFp+kvxSFY6WCj68V3g/OluZsuWF/iRkl+BoqdTvxgN42pnz5mhDhENFlYZNpy6RXVzJqHBX/J0suVwoRJvWUDuys6JKy7qT6bzWlEJxN4utFzy1R3C2zewNGiE9ZsCZ1Ybc9Y5jBEHQhO2QFSOIcLp1FnSJMmtVD4moVvPu/IhxTrlPP4MG0qyjELdFEIkLGimkaYKwiBvR+GrgrQ0zuRluFm5GIsEBNsJG28YLG436Hss8xqWSS9dMeWqtXNdB1+l0yKvLhpiYmGBicvU81taIjZkJa57pS3JuKdamcr3YkVIuY/XTffhxXxJ5JSru7+ahV0yXSCQsndaTb3YlcjGvlJHhrnpVYzMTGQUYQjZNTWRXnVSUqdTc89V+/Srm//69wObn+2NpWv/fUnPs7TEhvLgiGpVGi1Iu1RtLL3tzds8ZzP6EXJyslA1+GYi0TWRSmZFzDvBSt5d4dc+rVGmrkElkvNz9ZSQSCQWVBfXOL6goIL04nWd2PKNXRY7KjmKJ+RK6Onet19/Pxs+o1mQ/j376lcianbIaZ3nl+ZX8MfYPRvqOvGpd9RrqlnACIQ9eLpXrc43qllcqriomszQTe9P6q921j/na+F7ztRuTtm4zAWYP74i9hYJ9CbmEuFkza4hxlErdHffHevuw6dRl4qpr4I6OcKW3v/D/efGuDjzex4crZarrlmlr9UilgpDQ+qeEcj0OgTDyE8PzHe4SHm0MC5P6kWoWJhbklucyc/tMvUBPVHYU9qb2DTrWdb9HK9QVmFaHi5o2EDZqJm/6hR2pUonPH8spO3oUdX4+mR/PJ+c/XwCQ/+uv+G1Yj8zKistvv0PhOkE1OPuLRXh98zWWgwYZXUtmbY3vH8vJW7IEbWkpdhMnYhYRQVVmZr3Xlds7ILWwwOm558j+7DPQ6QRV9hcMYnjKDh1Qdmg4bU1m03AahCo1Ve+cN4hOh6awsFEd9NZqL80aENQ1V8goqqji0Z8O60V4o1KvYGNmwpjqcpI6na56/mY4v+5720Qm5e2xobw9NtTo+APdPVlzMoNTaULu+sAgJ+4KcWbtyfR6Y4nPKuaRXt480ceHpYcELQRHSwVPV6cA9Qlw4NDcoaTkluHnaGGUCiSRSBosP1yXyYuP6quA/HdnAitm9sbURGZUHaguDc1vWwSWdRbvrVwERzppj5A25N0L/n5TyDkHIe/8gZ/g0bWw/z9CpFTIOMOiaq+ZwnlxmwUxudrl1cxsDVFQIvWQSqS83/d95u6bS15FHt5W3rzR8w0A7OtEMSikCiwVbXPecN1PSvfu3XnhhRfo3r07J06coGvX+hP2lkh8VjG/HxbKrD3W24dAZ0uyiyvILqok1M26niJ6Q2Jq7rZm9XLDa3C2EoSV6vLS3UG8vva0ftXw5bsMX5AarQ5Zrdf9OybTKMQop7iS1SfSmTUkkFHhrnoBD0dLBdP6+wEwKsKNHn72xGQUEuFho19QAGEx4K5QQ7mjulwpU2GukBsZYp1Ox5HkfIor1Ayors0u0voZ6j2Ufx74h9M5pwl1CNXXKh8fMJ4tSVv0Cpaelp5EukayLn6dUckiHTp2pe2iq3NXtDotRzOPUqoqpZ9HP0zlpkyPmM6kjpMoV5cbCa6tjltttJMdVxDHoUuHjBSWb4RKTSXvHniXf1L+0dcrHhcwjv4e/Y1KePhY++Bj7YOHpQeR8ZEczzoOCLWVx/iPufk/XCPQWm3mzSCTSpg+wJ/ptXa7tVodBWUqI5tUg625gi0vDuBYSj6WSrlRpQwAewuF0U5NQ8RkFJKaX0a/AEdszFvHJL5BAofBy+eE3HRbbyE8q43zQIcHWJewjrRiQT+lq3NXBnoO5O/kv/XOeQ270nbRz6MfBRUF/B77O9ll2Yz2H62vPnG55DKv7X2N6Jxo/Gz8+Ljfx0Q4RTDabzRbkrcAgvM/JWzKHbk3iVSKRe/e5P30E9o8Q6m/qowMiv/5B8tBgyhcv95wgkZD3s+LjRx0nVqNRC5H4euL23vvGV3fasgQLPr2ofSgkJuqDA3BR4i8yQAAIABJREFU5r77AHCY9iSWQ4egSkzEPDLyqoJy6oICqtIzMA0J1tdprzleevAgCm9vzCIiMA0ORu7sLKjQN4BpaCjK4Mbd/Wyt9nJIsDNdvW31Qm9uNqZM6unFkaT8ehVytp3LZEwnN7aeucy8TWfJKa5keKgrn0/srBczvnSlnGMpQs3zugucxRVVyKVSzBVyNjzbl+MXC1DIpPqNmH6BjihkUqPw98Edhe/lD8aH81APLy5fqaBvoAPmCsP/XymXGeWEl6nU7I3PxclKQXcfgyOUXVTBrwdTyC8VKm308LXnVNoVoxKdlWotSw6msGhSVyI8bOrl0YOQfz+pVonOFo9cCUHDhd8ri+HoD7We1MGBRcKO96hP65+r00HEBOj80NWvX3RJKL/mEt4uvgduhj7ufdg+YTvZZdm4W7jrF7Ge7fwsUdlRFKuKkSDh6c5PY62wvs7VWifXddBff/11du/eTWJiIg888ACD6qz6tkTS8su47+sD+tIPa0+m83hvH37Ym4Raq8PfyYLfpvW66bDv3XHZHE8poJuPrVHd39pMjPSiq5ctx1IK6OptS4ibNaWVal5be5q/YzJxtTblg/FhDAtxMQr9qUFXffCbR7txOCmf3JJKBnd0wsrUMCF1tFQyuOONh+oWVVTx3PIo9sbnYGNmwjtjQ3mwuyc6nVBreFecIAjiaWfGumf76sNSRVo3TuZODPMZZnSsj3sffhz+I38m/omDmQOPhTyGidQEH5v6IeW+1r5odVqe3v40hy4Lk0N3C3d+G/0bTuZOWCos661c1lVmvtqxGtKL03nv4HtEZ0fT1bkr7/d7Hw9LD5adW6afbOdX5PPugXfp5daLZzo/g0anYVfaLnysfZjdfTZSiRSFTMHiEYuJyo5Co9PQ3aX7NV+3KWmNNvN2OZlawIsrokjLL6eDsyXfPtat3iRTJpVct1JEVlEF66MykEsl3N/NU++0f/TXOX7anwyAlamcP2b0rufktyrkCkO4Y3GWUBYoaY8QMnnP/8C5BYWANgJ2pnasH7+e/en7UcqV9HHrg0wqw9fat15fH2sftDotT/7zpD5iZsOFDXw17CsGeg5k/pH5ROdEA5BcmMwb+97gr/v+4pMBn3Bv4L1klmYywHNAvaiiJqcheyORomtoO7G6BFvZySguv/02qqQkLPr1w/3/PkXuYPwZkcjleC9eTFlUFDpVVb3ya0o/P5R+flcdVsGqVWR99DE6lQq5uxveP/6IMiCA8jMxpE6Zoq93bj95Mi5z38Drxx/I/vxzqjIuYT1yJKahIRRv21YtZvd4o1dDaK320kQmZdVTffj3fDblKg13hbpgqZTj56iu19ff0ZLCsipeXhVNRZXwv//7bCb+uyx4bWQw289l8ezvJ/TaRvPGhTKlnx8arY43151hzcl05FIJTw0K4JW7g+hRp9KPp505P06O5H87EyhTaXiijw/dfOxYcjCFkko193R2JyxUsJdJOSW8sfYM0elX6OVnz/892Ak3GzPS8st44NuD+pK/Yzu58dUj3VCptTz43SFS8wXdmVXH0/hjRm/MFPU3c6TV741l03qyeH8yaQXljAxzRWkipaJKw+COzq18E6jOZ7mhSTzAoW9gz6dCfnqP6UIt9Lqfmx3vCw6+TiuEyT++ASzusM1q4ZhITfCwNFb+D3MMY9sD2ziZfVK/QdNWkc2bN2/etTr8+++/nDlzhmeeeYYPP/wQBwcHvL1bxgpYbm4uTk71yyUtP5KqdzpBKE12/GKBPuymoKyKMpWGu0Kuvttcl292X2DOmtMcTclnY7RQyupqk00HSyURnjb6fKEvdsSz/EgqOh0UV6jZGZvFE318CHSxYvOZy3o1ZAcLBR/fF4GlqRyJRIKXvTkdXa2MQqFuhS+2J+hDoCrVWnbH5fBIL2+i067w+XaDemVRhRozExl9AkQj0ZbxtPJkmPcw+rj30Yeeelh6kF+RT2x+LDp0DPMexnNdnuNY5jG+PfWt/tziqmLMTMyMRN7qXntT0iZ9ibZQh1Be7PYi2WXZfBUtqGaaSE3wsxEmlC/uepFjWcfQ6DRklGQQXxDP+MDxLDu3TK8eD6BFS2+33vjZ+tHHvQ8PBz/MaL/R2JoadowkEglulm54WHrc1iTyanblRmmpNvN27+taPPbTEX3d8vxSFXGZxUyIbFhBX6fTseJYGv/bmUB8VjHhHjYo5TIyCysY8999bD+Xxd6EXP46fZkHu3tSWF7Fc8tP6qdGKrWWgjKVvi5wq2fDM0I+ok4j5KZfPCSU72ljyKVy/G398bb21i+euVi4UK4u50zuGXTo6OfRj1e6v8K5vHP8HPOz0fkqjYqRfiP57PhnRiKVhapCHgt9DFO5KV5WXoQ4hDRanfObQeHjTdFff+kdXhMfb1zefAu5vR2qixepjK/+rpVKcXnrTRTe3lx89FGq0oSogqq0NNS5OVjffXeD1zdxc0Ph6YGkgXrbV0NbWkrqtOnoKoT8f21xCeqcHKxHjyLzww+ojIvT9y0/cwbbCRNQ+vhgM24c9o8+gkWvnij9/bG6+24sevVCatbwpsbt2JaWai/h+vclk0oIdLYk2M1aH5lYs6h4onrO2dPPnvfuCSU+q1hfgacGUxMZ93fzZNbvJ/WOMcCJ1AJmDPDnz1OX+M/2eHQ6IQLzSHI+Azo4Nri55OtgwcQeXjza24cOLpaM/+oA605mcDAxj9XH0xkZ7oqdhYIpvx7jZGoBGq2O1PwyknJKuLerB19sj+dAoiECJD6rhLtCXIjPLmHJwRT98Ro7/HgfX46nFOgdd1MTKfPv64SLtSmm1fPIkeGuBDpb4utoQaCzVcsR8LwV5EooyTEunTn8Q3A11pLgUhSselwQ+9RWQdoRQWfEuVZ1gtwEWDMV/V+zJFtY4AsY0uS30RbYnLSZv1P+JrM0kxCHkAZTnFoyN2ovr7uD/r///Y+lS5cCsGjRImbMmNHia1TaNhD+WHehK72gbvXSa/PzvuR67ZfuCgIEVcytMZl425vz7OAAvXBbDdF1al2WqjQkZJfQzduOP2f1Z+OpDFRqLfd0dq93bg0arY6PN8fyx9FUrM3kzBkRzIPdb6weaexl4xq6Ko2WC9kl+oWB2jR0TKTtI5FIeLv32zzd+WnUWrW+BFLt8kY11BzbfnE7nx37jNzyXMYFjOOtXm/hY+3DhvEb2JayDSuFFSN8R6BDx9S/p5Jekq4/b9GQRQzzHmYk7ARwMlv48uvh2oMdqYb6pGZysztSNqkxaI0283aoVGtIrJWqA/VtTm2+25PEp38LNY+3ncsiOu0Ky6b1Yn1UBnmlBkXcjCvlbD1zmUhfu3o5jVfK2pCdSq2j6p59FiqKwLRthu3VZXbkbKaGT6VcXa7fLbFW1r93G6WwA9jDtYc+ugYg2D64RYQ4yh0c8Nu4gaKtW5HI5FiPHoXMUlgAdf9kAVZDh1CZnIzV4MGYhoZSdfky6jr55RWnBbEpVVoal+bOpfzEScy6dMF9wXwUvr4AlOzdy5XVq5FaWeMw7UmUAUJOcfGuXWTNX4A6MxPrMWNwfX8e6vx8dGXGcx1VhmCHtUV1bLtWq19cuJO0RXv50l1BTO7jS3GFGm8HYbEozN0aa1O5Ufh7zSZPcZ2Q+HKVBo1W16AdPXe5mK5edizYGsvvR1KxUMqZM7wjE3sYFkR3x+UYCQ2XVKpZcSyNV4d31Oev13A8RdCludp80Nas/ny65tjiKT34+2wmWYUVjAx3xcv+zi+M3TCJu2DLq1BwUVBIH/dfUN5k7vLoheA/GLLOCiJxlk5C6baSbOj0EITdC+nH65+XcQLC7ze0r1ys36ehYyL1+D32dz45atBticqOYsmoJc04oqbjustZcrkcKyshVNHKygrpTazeNhfju7gT5m74wu7saYOnnfGK4+gIt5u6pqxOzrpMJrSXH0llzprT/Hs+m18PpvDE4qP6MPUaevoZhyNZm8oJcRXGZ2NuwhN9fJk+wF/vnAthnunE1MrhWXMijcUHkimv0pBVVMlra05xMe/GvkwHdDDeEbc1N6GTpy2DOgq12WswkUl44AadfpG2iaOZo1F94v4e/Y3aJlITxgeMJ688j9f3vs6l0kuotCrWJqzlt1ihTrOzuTOPhT7G+MDxmMpNic6O1jvnNWxOEmoAd3bqbHS8pv1Qx4eYGj4VRzNHOtp1ZNHgRfoJekunNdrM20Epl9Grjo0bGHT11eH1UcbvhX0JuWQXVyCX1o96kMukBDpbEeljXJJyUs+Gd+dbJV51olGcgluVc55bnsumxE2czTt7y9ewN7U3CmX0t/Hn/g73Gz1fk1M+t+dcRviOwEZpQy+3Xnw26LNbft3GRm5nh/0jj2D30ERkVoYUD4lMhvWoUTg9+yymoYLwl9zFBRMv4/exeY9IAC6/9Tblx0+ATkd5VBSX5r4JQOnRo6Q99TTF23dQuG4dFx95FE1xMZqiIjJemU1VWhq6qioKN2wg78efUHh5YRpmrJVjPUIQ67SdOLHOa/e4Zqh8U9FW7aWdhULvnAOYK+T8+EQknTxtcLRUMKWvLzMHChoej/Qyjhi4r6sHpiYyBnQwtqMyqYR+AQ6si8rgx33JlKk05BRX8vq60yTmGBzyBm2pVIJCLq1X2rJrtW2dEOlJ7dP8HS3o6WdPZy9bvcAdgKu1KVOrNZEUcin3dHZnxkD/lu2cq8pg9WTIuyDsasesFULQGyLtKJxeJVTSqItEAiFjYfDrQtm2X8bAiV+FCKjVk4USaT79gDp/f586opfefcGiTppq2H23enftik2Jm4zaJ7NPklGScZXerZvr7qB36tSJ2bNn06VLF06fPk1oaOj1Tml2zBVyNs7qx4HEPKQSoabkpSvlfLkzgYyCcsZ1dufhnt5UVGn46t8LHEnOo4uXLS8M62CU612b54YG8u5GwwTkuSGBAGyMNn5jnM8sJi6rmGBXwwTrmcEBZBdXsvn0JTzszHl3bGiD+TsABy/kMvXXY1SqhTylF4Z14JW7gzhx0Vh9W6uD6LQrDdZxr8vUfn4UllexMfoSrjamvDEqWP/666rrWhaVVzEx0otOnqL6u4gBcxNzfhv1GyvjVlJSVcK9gfcS4hDCvvR9RqJyAKdyTjV4jYZyQWuOfdjvQ94+8LY+B/3Dvh8CgjL9K91f4ZXurzTyHTU9rdFm3i5fTurK+5vOcjq9kF7+9rxbrT687PBF/jp1CU87c166qwNe9uY4WSmJzzJMJi0UMqyUJtzfzYPFB5K5XCiE4/o7WTAqXFgc+mVqD5YeusjFvFJGhbsxJLjpy+XdMUZ/BqoSSN4Lrp1g/NfNPaIb5njmcZ7Z8Yy+hNr0iOm82O3FRrn2+33f58EOD5Jdlk0f9z76sHVbU9sW5ZTfKhKpFM//fknm+x9QmZCA5aCBOM+ZA0B5dLRR35p20ZYtRuGAmsJCSvftQ+boaFRurfY5nt9+Q+7X36BKSsJy6FDsJwsq0zZjxyCzsaZ4+w4U3l7YTnq4ye71WrQne9nL34E/n6sfHTBrSCBe9uYcSMgl3NOGSdW74QODnPjo3nB+PZiCqYmU54Z0wN/JUq/HUYNOJ0RqBlRXwRgY5GQk1GZvoeDhnsIiwBcPdebV1ac5VZ2DPv8+IUS7b4AjK5/qw7qTGThaKpjc1xeT6pD0rx/pxpP9CsgrqWRgkFPryyPPjRNqkNcm/Vj9fptfhWM/Cr8rrGDyn+DRreFrph6Cojrq+WfWwMQlgg3f86kQ5t7rKQgeDRd2wpHvQCKDvs/BlL9g72dQmi2UZAsdf/v32Q6oO59UypQtIoKqKZDo6m73NsCOHTtISkoiMDCQoUOHXq87Wq2WefPmERcXh0Kh4KOPPsLHx5DIv2fPHr7++mt0Oh1hYWG89957aLVaFixYQExMDCqViueff54hQ66djxEbG0tISMg1+1yLN9aeZsUxQ07QqHBXvn2s+1X7R6UW6EXiahQuX1oRxYbqnHQQVikPvzkMxwaUjGuTU1zJ59viiL1cxIAOTjw/LBClXMZD3x8yUsZUyKWcePsutsZk8tqa0/rjMqmE3a8ObnDVctXxNBbvT0YukzBrcCCjbjJaQETkeuRX5DN8zXAqNYa8uVcjX2Vy2GRKq0rZm74Xa4U1fdz7IJVI+eToJ/we+zsg5Lv/MuIXvbJ8S+N27QrcnM1sLfbyZvn9yEXeWh+jb3vbm7Pr1cGcyShk8uKjFJZXIZNKeHdsKJP7+gJQWFbFlpjLyKQSxkS46RWORVomM7bN4PBlQ4i+XCpn98Td2Cht2JK0hVXxq7AwsWBGxIx6JRSbEp1Ox7HMY2SXZzPAY4A++qZYVcznxz/n8OXDhDqE8lqP14wihFoKF6dOpeyQ4e9qFtkd399+I/e778hZ9KVRX5/lv6Ps0IELgwajrRXO7vTiCzg+88wdGe/t2paWaC8b476aivVR6by80rAgLpXAztmDcbMx5d/z2SjlUnr52bMjNpviSjWjwl2vOydt01SVw39CoLzWRlf/l+GueYZ2YQZ8EYaREFzIOHjoN2E3/d8P4fIp8B8Eg96AK6nwdQ/j1+k9C0bOr//6mTHw/UBBZwRApoBnD4NDQP2+2edh29uQlwDBY2HYu0L+uwgA5/PPM2PbDK5UXkEqkfJyt5eZEj6luYd1U9yoXbnu7CcrKwtfX1/8/f356aefcHNzu+6Fd+zYgUqlYuXKlURHR/PJJ5/w7beC0FRJSQkLFy5k6dKl2Nvb8+OPP1JQUMDu3btRq9WsWLGCrKwstm7deoO3eutsOXPZqP3P2Uw0Wh1qrZb5m2PZfCYTTzsz3hkbSncfO7p6C4/avDCsA0eS87lcWIFUAi/fHXRDhnDW7yc5miI44qfSCymv0vDO2FBKKo1zkao0WirVWh7s5kliTgnLj6RibWrCayM7NuicH0nKM3LkZy0/yd8vDbyhmpYiIjeKvak9Cwcu5PMTn5NTlsO4gHE8EvIImaWZPLr5UbLLhTI9vd1688PdP/BGzzeY2HEiuWW5dHXpiom0FZfJug43azNbi728WbaeMc6vTc0vIyajkC5ethx8YygnUwsIcLI0EjyyMTfR7/SItHxql1QEUGvVVKgrOJt3ltf3va4/fizzGFvu34KjmSNHLh/h8+Ofk1Oew1j/sbzY7UXk0sZdiHl97+tsTRE+EzZKG5aOXIq/rT+fHP2EPxP/BCCjJIOcshyWjV7WqK/dGLh/9BGX3n5byEHv3Bm3+R8DYPfwwxT9s43KWKHUpM2992LeTdjh8/hyEVnzF1CVlYXNmDHYT2sdQoOivbx57u3iQXxWCb8fvoiVqQmzhwdhY2bCiEV7uVgt1tnFy5ZVT/UxKqvbbjExg4nLYMscIdc7dDwMfM24j6qEeirtldU6DWumQvIe4fdLJ4XjYz4XHPLD3wjnOQRC3+fh7HrYtQCqygSxz/4vw/nNBuccBHX3uK3CTnpttFr44yEoSBHah74SnPNh7zbSH6L1E2wfzD8P/EN0TjS+1r64W7YRsdgGuO634uzZs3nuuedYvnw5I0aMYP78+Sxbdu0vtBMnTjBgwAAAunTpQkyMYRclKiqKoKAgPv30U9LS0pgwYQL29vbs37+fDh06MHPmTHQ6He+88851B19ZWUlsbOx1+10NJ3MpRRW12hZy4uPO83t0Ab+dElbacksqmbr4MMsmeKO4igLl9+PciMutwMXSBCeLKqMxZZVUcSi1DAdzGX28LZBLJRRVaPTOeQ1/RafxYICEod4Kzho25OnjZU5OWhI5wL2+cK9vzeS1iNjY+gIi604aX1erg7X7z3JvaOvI3xVpPbjiysLghfr2hbgLLE9brnfOAQ5fPsyao2sItxbC6Kyw0pdOaqvcrM1sLfbyZrGUVBq1ZRIoyUkjtkRYGHUECi/nUni5gZNFWgX9rPpxJveMvt3Nthv5qfmsTVlr1K9cXc7a42uJtI3k+ejnKdcK4di/nv0VbbGWsa5jb+n1KzQVHMo/hFqnpo99HyzllqSWpeqdc4DCykK+PPglT/s9zZ6Le4zOj86J5mTMScxkN1dy9Y7wmuBAlAGJJSVQ89n9+CNISABzcwo9PSmsOe7oCP/5HIArwJXExDs/5lugpdpLuPM282a4xwfu8amZDxbz1ZY0vXMOQgrkkh0n6O9zk0JobRZHGPoLAKb55zHfPJ8K+2DKnA1Rs95OXbHIMQjXprsOp/T0cTomG9uNqjMbuOA/HXwex8RhCPKKfMrtwzA5f4aArdOQ1DjjO+aRXq5EojbBuFgYpJXKKanz3lIUpRBQ45xXUx7zFynuE27v1tsgdthRWFhIIYXX79xKua6DLpFI6NGjB9999x1jxoxh1apV171oSUkJlpYGoyCTyVCr1cjlcgoKCjhy5AgbNmzA3NycRx99lC5dulBQUEBqairff/89x44dY+7cufz+++/XfB2lUnlb4UcLTF14atlxCsqqsFLK+WRCV0I6OhO/95BRv6JKLdh4EOJli06nIym3FGcrpVG+ekP60mfSC3lm+SHKq4QP6+COTvw6tSdqjRbHzZfJLTEoFge52RISEkJICHQOyuLfuGyCnC2Z1NP7qvk+R5Ly+Hp3IhUqDY/38WFcZ3f6qS6x8oyxMvagzoGEBIql00SaHtNCU6jjcDm4ORDi3fLCBK/G7U7IbtZmthZ7ebO86+ZLws9HSMwpxUQmYc6IjvTr1kBIXzVXylRcKavC1/H6uhoiLYOQkBAi/CPYk7YHPxs/Hgx6EFO5KV10XdiWvc2ob+/g3pSry/XOeQ0XtRev+b7U6rQsjlnMzos78bLy4rmuz+Ft7U1ZVRmTNk8iuVDIx92YvZGVY1dSWVwJMcbXMLEwEb5f00KMQvI9LT3pGta10Wt7NzktLE/7dmxmS7WXcOdt5u1gkRYHGG/Q2Di6EhLShgQ1G4Oo32D7LEN78FwY/Ibwu/8mOP6LsIMdeg+e/oOFXe3tblBsmNiYuHQU3hdVFXBFCvaDQWYCJ6OMd8oBT1USjJgPBUchvnrhMOx+vIbNEMToTvwqiIIOnAMBg+BfG6g0OJ1mPpGt5j0ocmPcqL28roOuVqtZuHAhkZGRHD58mKqq65e3sbS0pLRWuQ6tVotcLryUra0tERER+hpwkZGRxMbGYmtry+DBg5FIJPTs2ZOUlJQbuoHboaefPYfmDiMhqwR/Jwt9vmOEhw1Ha+WBWyhk+DtZkHGlnKm/HCU+qwQzExnz7gnloR5XD8esUV2vYXdcDmcvFRLmbsNH90YwZ/UpiivVeNia8dYYwwfwrlAX7go11GhXa7QcTcnH3kKhF5+7XFjOE4uP6sXkjqbk42ipZGyEG4cS81h9PA2pVMLUfr70FZ1zkTvEfR3uY23CWr2AnIelB/08+l3nrLbFzdrM1mIvbxZ3WzO2vzyI2MwiXKxNr5n68+3uRL7YHo9Ko6Wrty2LJ/fArrqesEjLZqDnQAZ6DjQ69mDQg+zP2M/BSweRSqQ8EvwInZ06k1eeh4nUxEhgMsxBUBlXa9WczDqJraktQXZB+ueXnF3ClyeFvOuYvBhi8mL4676/2Jm6U++cA2SXZbPxwkamhk8l2D6Y8/lCKT+pRMqDQQ8C8GavN3ll9ytcuHIBVwtXPur/UetzztsYor1sHO7v5snP+wVldwBnKyUjwlqevkKzs+8/xu0D/4UBswUHu6oCnEOhy6NgIZTAQyqFsYtg/UxBaM7KHUYuEMq2rXkSyvPByg0e+h3cG9DZcO8KcgU8sgLyEoV65/Z+EL8NNjxt6JdyAF46Dfd+DZtegrJc8Owphre3Y67roC9YsIADBw4wYcIEduzYwaefCqUJVCoVCkXDE6hu3bqxa9cuRo8eTXR0NEFBhi/bsLAw4uPjyc/Px9ramlOnTjFx4kTy8/PZs2cPI0aM4Pz587i53RkBKVMTGRGexuHfLwzrQEpuKf/GZeNspeSD8eFYmZrw3p9n9erD5VUa5v15jlERblibmrDs8EV+2JuIVCLhmUEBTOrpjbpu8V6EeuYAI8NdGRjkSHpBOQFOlvXKuNWQXVzBQ98fJjlX+EJ6sLsnn03ozN74HL1zXsP2c1n0CXBgwf0RvDk6GKlEIoosidxRQh1C+WPMH2xM3Ii1wpoJQRNQytqXwMnN2szWZC9vFqlUQpi7wb7+sDeRJQcvojSR8uKwDozv4kFafhkL/zmvr3UelXqF7/cm8cao4GYatcjtYiY34/u7vyetOA0zuZleedfBzIEP+n3AwmMLuVJ5hWHew5gSPoXc8lym/D2Fi0VCLeDxAeP5qP9HAOxK22V07bTiNBIKElBrjfVaADQ6DVKJlJ9H/MzquNXklOcwym+Uvnyjn40f68evJ7c8FzulHTJpK1OjboOI9rJx8HO04M/n+rPqeBqmcikP9/LGpoEa5u2eunZDpxVk8M9thLXThfxwuSlM+BU6jhL6dBwJr5wX8tcdOoBMDl92FpxzEHbXt74GM3bC8I8NCu5dH4OIibDzQ2Hn3txBEKaz94O4zcbjqCqFpN0Q8SAEjRQWAyzEzbX2zA2puDfEE088wdKlSxt8rkZlMz4+Hp1Ox/z589m7dy/e3t4MGzaMzZs38/PPPwMwcuRIZs6ciUql4r333iMxMRGdTse8efMIq1O/sy5NrbBZUaVBIZMirXae7/vmAFGpV4z6bHt5IAWlKh764bDR8fXP9kWr0/HwD0dQaQRHuqefPaue6nNTY/j07/N8u9s4l2zTc/0pr9Iw8XvjUPyP7g3nsd4+iIiI3DpNZVeuZjPbir28HjvOZTF96XF9WyoR7GdWUSWP/nTEqO/wUBd+eCLyTg9R5A6h0Wqo0lZhKjcFYNGJRfwc87NRnxVjVhDmGMY7B95hw4UN+uOmMlN2TNiBTCLjwU0P6mvg2intWDVuVYtUZW/rNIVtaW572VT3JdLeHaF2AAAgAElEQVTMHP0RtrxqaPd9AYZ/CF+EQ6GhqhMOgfD8iYavoamCD50wEpUztYE3UqufVwuh7nIlnFgCm14w9JMp4eUYOL1SUGuvzcw9De/Ci7QpGk3F/Wpcy6+XSqV88MEHRscCAgy5h2PGjGHMmDFGzysUChYsWHCrw2kS6uZ+3x3qYuSg+ztaEOhkyX/PJNQ793BSPs8MDmDLi/3ZciYTF2sl47sIMhGVag0Xskvwd7Q0qodeWqlm5/lsLBQyBgU5IZdJySmurHft7OIKhoW4MKWvL0sPpaDVwbBgZx7s7tlIdy4iItLYXM1mthV7eT0OJeUZtbU6wU4+0M0TBwsFeaUGTY7hbSU0My8RLh4Uaum6XMMhUKsg5zzY+4Oy7Ys6yaQyo93r3PLcen1qjs3qMouY3BguXLmAmdyM13q8pi+btmLMCv5K+guVVsVY/7E4mzvfmRsQaXLau70UaSJ6zgCnYEjeKzjDwdXvlZJs437FWcJPtQoStoG2StjZNjETwuE7DIeEfwz9g8carlOWD87VEWAp+4yvq6mEtCMQ+aRQGz1pF0jl0GeW6JyLGHHLDnp7zNt6amAAOp1Qjs3HwYJXhwchlUro5FlfIb3mWKCzFS8MM5Q4O3GxgKeWHSe3RIWVqZz/PtyVIR2dySmu5N6vD5BxRRDQ6e5jx8qZvbmvqwdrT6ZT813lYq2kX3VO+bx7wpg1JJBKtQZPu/ol10RERFoO7dFm1uZqdtJMIeO36b34Yns82cWV3N/No20sNsasFUImddWpSCM/hd5P1++XcRL+eBhKMkFpDfd9Z5g0thPGBozlz8Q/0VXvSDmbOdPLrRcArhaurLtnHSlFKTiZOWGpMCxg2Jra8ljoY80yZpGmpb3bS5EmxG+A8KhNp4kQVat6QOdJQv30xSOE+ucA9gFCGLuZnWCnd34glF3z6Q9D34J/P4Z9nwu75+7d4LG1Qg76mdWG60qk4NoJFBbwxAZBkE5hKYazi9RDTFC+CWRSCbOGBDJrSKDR8aHBLjw7OIDFB5KRSiTMGOCvd6Lr8sGms3r19uIKNW+vj+HAG0P542iq3jkHwZHfFZfD3aEuLJnak9Un0rE3N2H6AH+jnX0nq/aV3ysiItI6GdfJnajUKyw/mopSJuX5YYF08rQFIMTNuu2FtO+ab3DOAXbPF3Zv6uY9//OW4JwDVBbB5tnCTk07yo/u7dab7+76jg0XNmBrasvksMn68HcQnDU/G79mHKGIiEibZsx/hLD29GPg3Qd6PSXUNK9xzgHyE+HUCuj9DJjbw7hFhudyE2Dv/xnal04KdcwHvQGZZwQn3dRGyEG3q5WKaufbxDcm0lppkhD39shrI4N5+e4gJIC8ul56QlYxh5LyCHO3pruPPQAX88uMzrtUWI5KrdUrb9amTCWIWQwMcmJgkNMNjSMxp4RfD6RQXqXh4Z7edPexu427EhERaSzau82USiXMuyeMN0eHIJUY7GSbRWVs66mqqHbY6zjeBcnG7eLLws7N1ULdS7Lh/GawdBHCLGVtY529r0df+nr01bdzynJYdm4ZWWVZjPEfU08pXqRt097tpUgjoFbB0e8h/Tj49IMe066+8ClXQP+XjI+pSur3qzmWdQ4uHhB2yD0jhZ3wuuQnC9e97zsY96UQyt6OFl5Fbo/rzpAyMzON2klJSQAEBgY21L1NEJVawMhFewl4cwvTlxyjoFZu5LUwkUn1k85Npy4xYtFe3t14lge+PcR/dwp56qPCjXMrhwW7oJBLebC7B2a1dsbdbEwZFuLCzVBQquKBbw+y7PBF1pxIZ9IPhzh3qeimriEiInJ7tEebeTMo5NIbcs7VGi3vbIgh+J2t9Fmwk43RGXdgdI1IjyeN292eEHIX6xIyzrgdMPTqznn2efhfJPz1Eqx4GP6Y1DhjbWFotBqe/OdJfjn7C1uStzBr5yz+Tf23uYcl0gSI9lKkydj8iiDEdm4DbJ0D294RjqtV8OcL8KEzfBEB5/5s+PyQ8WBRa3NMaS2osp9aCd/2FcTmfhoG+78An76CSnttQu8x/C5Xis65yE0hmzdv3ryGnoiPj+fcuXO8+eabeHl5kZqaSnJyMvPmzePhhx9m8ODBd3akDZCbm6uvd9lYaLQ6Jnx3iOTcUnQ6SMotJa9UddOiRc//cVIfyg5wKv0K0wf4MSjIGa1Oh0arY0SYK+/dE4apiQx7CyUjw10wM5ExoIMj8++LuGYd4G92X+DlldGsOZGOq40p/k6WbDp9iU2nLuv7aHVgaSpnQIfG/RuJiLRlbtWutHSb2RT2sjHIK6lkzprTvLfxLEeT8+nhZ4dVdenKL3cmoNbqKKlUs+NcFhMiPbEybSWlg3z6gVOIMMHrMR36vwIN5dX6DRJ+atTQcTSM/kwQImqInR9Aei3F+/wkCBwO1u6NP/5m5FTOKX45+4vRsSptFSP9RjbTiESuxa3YlpZuL6Hl2kyRG0CrhdWTjdOMchOg/8tw8H9wYJGQK15ZCOe3QORUUNTRclKYQ/j9Qo64V08Y+4VQIm3VE4YSawDpJ2DAqxA0AsrywNwRBr8OnR668fEm7YHVU2HPQigvEPLaRR2GNsmN2pWrxsYVFRWxZcsW8vLy2LxZqNcnkUh45JFHGm+ULZDMogqjXHCAk6kFAGQVVfDmujMcS8mnm48dH98XgYdtwxMpVZ0a5WqNDp1OUIZ/bWTD9X0Dna2YO9pYej+nuJJDSXl0dLGio6sgNrcxOoP/+zuuukc5T/92gr2vDcHJsn4+uqOFmKMuInInaK82syF+2pfE4v3JyGVSnhsayMRIr6v2fWPdGbafExRzd8RmUVxRxcqn+ujtbg1qrY5TaYW42VzFeW2JhN0rPGq4kiYo+Lp3BYdq5Wm5Eoa+LTyuh6aBaC5N/UofrR07Zf3ULHtT+2YYiUhTIdpLkSZFKhV2tEtqRWjU7IanHzPuq6mEy9EQeJcQlp5xQghbt/MFG09BAM6ofx07rK0CdOASChOXXHtc+z6Hoz8Ji7CD50KnCYJD/sfDQi10gL0LhRSmnjNu9q5F2hBXddAjIyOJjIzk7Nmz+nqRWq0WqbRt5w26WZviYWtm5KRHVudxv7bmNHvicwDYHZfD7FXRrJjZcF3zKX19mbfpnL49qadXvbJt1+NgYi5P/nqMiirB2Z8zoiOzhgRy8IJxuaIqjY6jyfmM7eTOkI5O7IoTxhjsasVDPa8+MRYREWk82qvNrMve+Bw+2hyrb7++9jShbtaEe9RXcQc4eMG4xNaR5Hw0Wh2RPnZsjL6kP24ik9DFy7ZpBn0nOPcnrJkKWjUgEXZjIqfe3DUinxTU4WsmiG5dwKt3ow+1ufG18WVSx0msiFsBgLO5M1PDb/JvJdKiEe2lSJMz4mNY/5Rgc2VKuLu6PJ9XLzj/l6Gf3BTcugoCcBueEXbdJVK473tB3b0uvZ6GbbWc9sgnG05fqkvsJiEKqob1M4XSagUpBue8huQ9ooPezrmuukxiYiIpKSmoVCoWLlzItGnTmDZt2p0YW7MglUr49rFuvLn+DPFZJQzp6MSb1bvah+vU8T2SbAhxicko5HBSHuEeNvT2d2BKPz/8nSw5cCGXUHdrxnW6dghiUUUVcZnFBLta6UM4F+1I0DvnAP/dmcCUvr6Ee1iz8rjx+WHu1sikEn6Z2pOo1ALKqzT08nNAJhVDZERE7iTtzWbWpa6d1OkEW1njoMdkFGJqIiPQWcizDvOw4WgtWxrsaoVMKuGRXj4k5Zay+ng6dhYmvDEyBFcbU1ot/35Y7ZwD6IR2t8nCTs/VKMsX8iflZhA6XgiznLlbcNItXaDLI9c+vxXzVu+3mNBxAjllOUS6RqKUidFgbZH2bi9FmpCIB4VUo8unwKM7WFbvoPd+RnCKT60AKxcY/jFYOAjOc01IvE4rtGsc9LJ8yIkD1wjo+xw4BkHKXiEaKuz++q9dVSE45BVXBNtt6Qwp+4376LSC0FyH4SCRCSH3Nbh2bvQ/h0jr4roO+tKlS/nxxx955ZVX2L17N08++WSbN56dPG356/kB9Y539rTlaIphItmpesK5+ngac9ac1h9/+a4gXryrQz319f0JuXyxI57C8ioe7unNtP5C2Zh/z2fx3PIoylQaLBQyvnmsO4OCnCgqrzJ6fZVGS0WVhkk9vYlOK2RDdAbmJjJevjuIQGdDrfWu3qJyu4hIc9EebWZtakqn1aazpw2llWomLz7K8YtC6Pq4zu58+VAX5t8XwfN/RBF7uYgAJws+myBMTGRSCe+NC+O9cWF3dPxNRvkV43ZlsTBBqygRdmOS9oBbZxi5QAirLLoEPwyGEiH8n4P/gxn/gkuY8GgHBNkFEWQX1NzDEGlC2ru9FGlirN2ER21kJjD2P8KjNhWFxu0am312A6x/GtTlQqm0ScshaDh0uBv2fQZf9RAWTIe9C969QKuBX8dARvVO2q6PYfpOoTZ6Xdy7Choi476E7e8IYwgZB31mNc79i7Rarrv0rlQKq9YWFhYoFArUavV1zmi7fPJABJ08Bac8zN1aP5H8ZneiUb/v9yZSpTHOQc8uqmDakmOcuFjAhewSPvzrHJtPC4Ju8/48py+zVqrS8P6mswA83NPb6BrDQ11wsFRiIpPy+cTOxMwbwcl37+bJ/mJ9WBGRlkJ7t5kjwlx4apA/piZSrJRy5ozoSKSvPauOp+mdcxAqXey/kEugsyVbXxzA6XnD2Tl78FVD4Vs93acYt7s8KpRI2/o6nFwKVy5C7J+CUBBA1G8G5xwg+yzEb71jwxURuRO0d3sp0oLoNtm43X2yIDa39XXBOQfBgf6nOrz9xK/w70eQlwAX98PvD0JFESTtMjjnIOSYH18MEROE8HiZUnD0RywQFmUBuj0Or16AuekwcWl9wTqRdsd1d9C9vb156KGHmDt3Ll999RUdO3a8E+Nqkfg7WfLnc/2pVGtQyg355GqtsTOu0QqCcLU5nJxPZR3huL3xOYzp5EZmYYXR8ctXhPbkvr64WCvZdT6HDi6WPNbbx6ifmUIs2SAi0tJo7zZTIpEwd1QIc4Z3RCKR6NNsLtexcwCXaml9WLcWdfZbZcibgjBcyj5hJ6VmMpi407hf+lFhd13bgKOiEZ0XkbZFe7eXIi2I4R+Bc7Ag5OnVW1hE1VZBabZxv6Lqkp8XdhgfrywS7LdWQz20GiEdadSnQki9RFo/PUkmFx4iItyAg75gwQJKS0uxsLAgPDxcLDkBRs45wPT+/rz351l9e3JfXxRy4w9esKsVdalRZR/b2Y11Jw01fsd1NoTjjAx3Y2S4W71zRUREWiaizRSoW+t8TIQbP+9PRqMVVi8tlXKGhjg3x9CaB4kEOk8SHrVxDjXeKbf1Ecr6dHkEjnwv5DAC2PlB8Og7N14RkTuAaC9FWgxSKXR7QnjojykheIyQT15D+APCT5dwY7E5iQycgoVwd+dQyK4WilZYGkdQiU64yA1w1TroNSQkJPDKK6/wyy+/oNPpKCoqws+vZYRUt5QalV28bOnpZ4+bjRnTBvgxpa8vkjr1Cx0slSjlMk5eLECj0zEmwo1XR3RELpMyKMgJmVSCQi7lgW6ezKk+LiIicue5XbvSUm1mc9tLVxtTunnbUqZSE+5hw4L7I/BztGy28bQYPLpD8j4oywVrD0E52M4bzGyFkEgLJ+g4CsZ8BqbWzT1aEZF63I5taan2EprfZoq0EDoMF37KTQXnffBckMoEBfasGMhPBKW1oBrvP1h4LmKiYM+9esLoz8CxQ3PegUgL4kbtikSnqxuMbczkyZP54IMPePvtt/nyyy+ZPn0669ata7SB3g6xsbGEhIRcv2MLolyloVKtwdZc0dxDERERaYDbtSst1Wa2RnvZrijOAgtHYXInItKKuB3b0lLtJYg2U+QGKcsHE3MwacVVRkTuGDdqV24ozsLHxweJRIK9vT0WFha3Pbi2zsHEXBbvTwbgyf5+9A1w1D9nppCJueMiIm0c0WaK1KOiCPZ8Chknwbc/DJhtPKGzcmm+sYmINCOivRRp8cSsE8Q8zWwF2+0aYXjO3L75xiXSZrmug25jY8OKFSsoLy9n8+bN2Ni0UYXdRuJCdjGTFx+lSiMEJuyJz2HriwP1NX9FRETaNqLNFGmQ9U9D3Gbh99SDQkj72C+ad0wiIs2MaC9FWjwJ22HNVEM7cRe8dFpQYhcRaSKum+gcFBRERkYG9vb2xMTEYG8vrhRdi23nsvTOOUCVRsf2c4IA0KUr5SzYEsvcdac5UavckIiISNtBtJki9VCrIG6L8bFzG4WfOh1EL4d1T8Ghb0BdeefHJyLSTIj2UqTFc26DcbviCiTtFn5P3gsbZ8HOD6Aku96pIiK3ylV30FevXs2aNWtITEwkICAAgOPHj99QjUqtVsu8efOIi4tDoVDw0Ucf4eNjKBG2Z88evv76a3Q6HWFhYbz33nsADBw4EF9fXwC6dOnC7Nmzb+femgVPu/q1C73szShXaXjg24P6UkOrj6ez+uk+dPW2u9NDFBERaQJu1Wa2Z3vZbpCZCIJBRemGY7bV/+O9C2HXx8Lvp1fA5Wi4/4c7P0YRkTuIOMcUaTXY+jZwzAcS/4Vl9wPVm3LnNsKzR0SVdpFG4arvovHjx9OnTx++//57nn76aQCkUikODg7XveiOHTtQqVSsXLmS6OhoPvnkE7799lsASkpKWLhwIUuXLsXe3p4ff/yRgoICiouLCQsL47vvvmukW2seRoe7sjnMhX/OCrvmI8JcGBnmyo7YbKM6wGqtjrUn00UHXUSkjXCrNrM928t2g0QCYz6HtdNBVQzmDjBygfDcyWXGfc+sgbGLQFF/sVdEpK0gzjFFWg09Z0DCP5B+TKhf3vMpQcF9zZPonXOAvAuQegj8BjTbUEXaDld10BUKBZ6ennz44Yc3fdETJ04wYIDwBu3SpQsxMTH656KioggKCuLTTz8lLS2NCRMmYG9vz+HDh8nKyuLxxx/H1NSUuXPn4u/vfwu31LzIZVK+fzyS5NxSAPwcBcETW3OTen3tRCV3EZE2w63azPZsL9sVHUfC7FjITRBq5NYIxJnZQmGqoZ/SUthxFxFpw4hzTJFWg5ktTN8BWeeEUpc2ntXHG0jHEAXjRBqJJonDKCkpwdLSIIomk8lQq9XI5XIKCgo4cuQIGzZswNzcnEcffZQuXbrg5OTEzJkzGTVqFMePH2fOnDmsXbv2mq9TWVlJbGxsU9xCoxGbI/y01uno7WXO4bQyAFwt5fR2VLX48YuIiDQtor1sb5hBUbK+ZRE0Bc/sN5BqVeiQkBk2kyvxF5pxfCIiLRvRZoo0DxKgGC4J7wkT51H4mK7HpCIXgCu+o7mcL4V88T0jcvs0iYNuaWlJaWmpvq3VapHLhZeytbUlIiJCX6Q9MjKS2NhYhgwZgkwm0x/Lzs5Gp9MhkUiu+jpKpbJV1ahcERrK8ZR8iiqq6BfoiFIullsTEWlp3OkJmWgv2zkhIdBjLKQdRuIagZu9P27NPSYRkZtAtJki7ZMQ6HxGEIyzdMHWszu2zT0kkRbPjdrL66q43wrdunVj7969AERHRxMUFKR/LiwsjPj4ePLz81Gr1Zw6dYrAwEC++uorlixZAsD58+dxc3O7puFsrUT62jM02EV0zkVERADRXoog1EAPHQ/2YsitiMj1EG2mSItBYQ7Bo8Gze3OPRKSN0SQ76HfffTcHDhxg0qRJ6HQ65s+fzy+//IK3tzfDhg1j9uzZTJ8+HYCRI0cSFBTEzJkzmTNnDnv27EEmk7FgwYKmGJqIiIhIi0K0lyIiIiI3jmgzRURE2joSnU6nu363lklsbKwYfiQiItKotFW70lbvS0REpHlpq7alrd6XiIhI83GjdqVJQtxFRERERERERERERERERERuDtFBFxERERERERERERERERFpATRJDrpI47AxOoNVx9OwNVMwa0ggoe7WzT0kERERkTuCSq3l610XOHAhlzB3a16+Owhbc0VzD0tEREREpL1zYgmcWQ1WbjDodXAMbO4RibQxRAf9NkjJLWXJoRRUai0P9/Qm3MOm0a69MzaLF1dE69v7L+Sy7/UhWJuaNNpriIiIiNwOUakFrDqehqVSzuS+vnjamTfatT/Zep7FB4R64ccvFpCUW8qyab0a7foiIiIiIiJGlF+Boz9AfjKEjBMU2usS/QdsesHQTtkPL54CubiALNJ4iA76LZJXUsl93xygoKwKgDUn0tn8wgACnS0b5fpbYzKN2oXlVRy8kMfIcNdGub6IiIjI7XAmvZCJ3x+iSiPojG6MvsS/rw7GUtk4XytbYy4btfcl5FJcUYWVuEgpIiIiItIULJ8IaUeE308th/t+gM4PGfc5t9G4XXwJ0o+Cb/87M0aRdoGYg36LbD+XpXfOASrVWjZGZzTa9b3t6+9ENXRMREREpDlYezJd75wDZBdXsjsuu9Gu71XH3jlZKTFXiGvKIiIiIiJNQE68wTmvIWpZ/X52vsZtiRRsvZtsWCLtE9FBv0Vszevv4tTkR6rUWvYl5BCTUXhD1yosq2LB1lim/nKUJQdT0Gp1TO7rS1dvWwCkEpgxwE/MQRcREWkx2DWQD15zrKiiin/PZ5GWX3ZD10rKKeH1NaeZufQ4O85lAfD2mBCcrJQAWChkfDg+DJlU0kijFxERERERqYWpNUhkxsfM7ISfpXkQ9zcUZkD/l8ElXDgulcPgN+s76Mn7YNVkWDcTLp9u+rGLtDnE7YhbZFiIC7397TmclA9ARxcrJkR6klVUwYTvDpFaPTG9v6sH/3moi/68wrIqyqrUuNmY6Y898/sJDibmAbArLoei8iqeH9aB9c/2Iz6rGGtTE1xtTO/g3YmIiIhcm0d7e7M+Kp2UPMHWDenoRN8AB05czGfK4mMUV6qRSODtMaFM6++nPy+7qAITmRQ7C8GZL1Opmfj9YXJLKgHYdi6LpU/2ZGCQEwdeH0p8VjE+DuZiaLuIiIiISNNh5Qp9n4cDi4S2qQ0MnAMJO2DlY6AuFxz48V/B0/sh6yxYOoO6EioKhf4Al0/BsntBqxba5zfD8yeE64uI3CCig36LmMik/DGjN4eT8lFptPQLcEAuk/L1rgt65xxgXVQGU/r50snTlv9si+PbPYlUaXQM7ujEt492p6RSrXfOa9h46hLPD+sAQJCL1R29LxEREZEbwdFSybaXB3HgQi6WpnJ6+NoD8Nk/8RRXChMTnQ4+3xbHwz29kEulvLgiiq0xmcilEp7o48u740I5cCFP75zX8OepSwwMckIhlzaq+KaIiIiIiMhVuft9iJgABcngN1Bwur/tJzjnADoNbHsHOk0CG09Y8f/s3XlYlOX6wPEvw74joKIIKCguKCFq7pmpZdpq7h47lR47x3at1DbNzJ9LZaWWZi7lrqVmai6477u4ISguqCiLoOzLMPP744GBgXErYBDvz3VxxfvOO+88M8k9z/2s/eHSbrCyg44fQ9u34dSqwuQcICcNItdB89fM857EA0kS9H/AwsKC1gEeRudupOWUuC4xLZvTsSl8v+Wc4dy2yAQW7LvEwNZ+ONlakZZd+MdcQ3rLhRAPABsrDR0bVDM6dyPdONnOyMkjPTuPsIhYw+KXWp2eObsv8FRQdbxcSsY7iYFCCCHMwqux+imQnmD8eGYy6HJhz/cqOQfQZkHYaGj0PLjULHlPF++yK6+olGQOeinr0dQbiyLTJGu42tEmwJPohLQS156LT8PO2pKPujXE2lI9qYqDNR88Vb+8iiuEEKXqpdBaRscdAqtS1dmW6PiSMTA6IZ0mtVzp37Jw/l5gdSdeaVO7rIsphBBC3N0j/YyPG78EVraQGGV8Xq+DG+cgpD/4FNkStMEzULdL2ZdTVCrSg17K2tT1ZMGglvx2+ApVHGwY1L4OdtaWtAnwwN7akszcPMO1nRtVB6B/S186N6rG+YR0Hqnlhr2N5e1uL4QQFdrrHQLwcLJla2Q8gdWcGdRezT9/omE1ft51wXCdjaWG9vU8ARj/YhMGt6tDckYuTX3c0MhicEIIISqCTqPBzUct/FazKbT8rzof+DRE/Fl4nX0V8G0FNo4waCNcPaIS+epB5im3eKBJgl4G2tb1pG1dT6NzHk62zB/0KN9tPktKZi79HvWlS36CDlDN2Y5qzjKsUwjx4OvZrBY9mxn3pLcJ8OTbPiH8svcidlaWvNGxrtFWav5Vncq5lEIIIcRdaDTQYrD6KarpAMhOgfDF4OQFHT9SyXkB79DyLaeoVCRBL0fNa7szf1DLu18ohBCV0AtNvXmhqczFE0IIUQm0+p/6EaKUyRx0IYQQQgghhBCiApAEXQghhBBCCCGEqAAkQRdCCCGEEEIIISoASdCFEEIIIYQQQogKoEwSdJ1Ox2effUafPn0YOHAgly5dMnp8+/bt9O7dm169ejFmzBj0er3hsejoaJo1a0Z2dnZZFE0IISoUiZdCCHFvJF4KIR4GZZKgh4WFkZOTw9KlSxk+fDgTJkwwPJaWlsbkyZOZMWMGy5cvx9vbm+TkZMNjEydOxMbGpiyKJYQQFY7ESyGEuDcSL4UQD4MySdAPHz5M+/btAQgJCeHkyZOGx44ePUpgYCATJ06kf//+eHp64u7ujl6v59NPP2XYsGHY29uXRbGEEKLCkXgphBD3RuKlEOJhUCb7oKelpeHk5GQ4trS0RKvVYmVlRXJyMvv372fVqlU4ODgwYMAAQkJCWLNmDR06dKBBgwb3/DrZ2dlERESUxVsQQohyIfFSCCHuTXnFS5CYKYQwnzJJ0J2cnEhPTzcc63Q6rKzUS7m5udGkSROqVq0KQPPmzYmIiGD16tV4eXnx+++/k5CQwGuvvcbChQvv+Dq2trY0bNiwLN5CuTkdm0JmrpamPlXQaCzMXRwhHnrlXSF7mONltjaPw5eSqeXmgK+Hg7mLI4T4G3G6HMwAACAASURBVMozZpZXvISKGTNFBZOXC5f3g1N18Kxn7tKIB8C9xssySdBDQ0PZunUr3bp149ixYwQGBhoeCwoKIioqiqSkJFxcXAgPD6d3795s2rTJcM0TTzzBnDlzyqJoFYZer+fNRUdZe+IaAEE1XVg8pBUudtZmLpkQojw9rPEyOiGN/rP2EZeSjYUFvNOpHu92Drz7E4UQD62HNV6KCujWVZjXHZIvqONHX4duk8xbJlFplEmC3qVLF3bv3k3fvn3R6/WMHz+euXPn4uvrS6dOnRg+fDiDBw8GoGvXrkYB9mGx+9wNQ3IOcCo2hcX7Y3i9Q4AZSyWEKG8Pa7ycuvkscSlqNWW9HqZuOUf/R32p5mJn5pIJISqqhzVeigpoz9TC5BzgwExoMQiq1jdfmUSlYaEvugfFAyYiIuKBHX604sgVhi0LNzr3Wts6fPZsIzOVSAgBD3ZcuZOK9r76z9rHnugbRufWvd2eRjVdzFQiIcTfUdFiS2mprO9LlJLlr8CplcbnXv4D/B83Q2HEg+Je40qZrOIu7q5j/Wq42hcOZ7fUWPBcSE0zlkgIIcrPC029jY4beDnTsIazmUojhBBC3IfgPsbHrr7g28Y8ZRGVTpkMcX/YxdzI4I9jV3G2s6JHs1om55VXcbTht/+25uedF8jIzaPfoz6E+LiZobRCCFG6wi/fJCwiDv+qjjwbXBMry5Jtwb2b+2BrpWHdiWv4VHHg9Q4BWFjIQplCCCEqmJj9cHYjVGsIQS+CxhLqPw19F0H4ErVIXNu3wcrG3CUVlYQk6KXsXHwaz0/bRXpOHgAL9sew7u322FipCqo2T4dWp8fO2pJ61Z2Z2DPYnMUVQohStf7kdf638DAFk6fCIuKZ3j/U8Hi2Ng+NhQXWlhqeD/Hm+RDv29xJCCGEMLPjy2DFfwqPo7fCC9PV7w26Q8ATYGmjknYhSokMcS9lSw7EGJJzUAn79qgEAH7de5HQLzbRePQGPlgeTm6ezkylFEKIsjFn9wWKrmyy9vg1rt/KQqfT89kfJ2kyeiNNx27ix23R5iukEEIIcS/2Tjc+Dl8EGUmQm6nmoY+vCV/Vg2OLzVI8UTlJgl7KLC1LDtG00lgQnZDG6NWnSMnSotXpWX74CksOxJihhEIIUXasNMYxUGMBGg2sDo/l172XyMnTkZatZeL6M4RfvmmmUgohhBD3QFNssLGFRv3snaYWidPrIOMGrH4TUq6ZvocQ90kS9FL2r5Z+VHEonHP+SC1X2tfz5OTVWxRfL//E1VvlXDohhChb/+0QgHWRhso+LXyo5mxnMt5JDBRCCFGhtR8GFkWGr7f4D9i7Qewx4+t0Wog7Vb5lE5WWzEEvZT7uDmwa1oG/Tl7Hxc6Kp4K8sLLU8Ggdd6wtLcjNK8zS2wR4mrGkQghR+h4LrMqGdx9ja2QC/lUdeTywKgBtAjyYvatwz1iNBbTy9zBXMYUQQoi7a9Ad/rcHojerReICnlDn63SAM2sKr7N2hFrNzFNGUelIgl4GPJ1sGdjKz+hcDVd7fhjQjK83RpKSmUu/R31LbDMkhBCVgX9VJ/yrOhmd69SwOp90b8i8PRexs7bknU71qFvN6TZ3EEIIISqIag3UT1EtBkFqbOEq7l0+B/sq5imfqHQkQS9HXRpVp0uj6uYuhhBCmMXg9v4Mbu9v7mIIIYQQ/4zGEjqPUT9ClDKZgy6EEEIIIYQQQlQAkqALIYQQQgghhBAVgCToQgghhBBCCCFEBSAJuhBCCCGEEEIIUQFIgi6EEEIIIYQQQlQAkqALIYQQQgghhBAVgCToQgghhBBCCCFEBSAJuhBCCCGEEEIIUQFIgi6EEEIIIYQQQlQAVmVxU51Ox5gxY4iMjMTGxoZx48bh5+dneHz79u1Mnz4dvV5PUFAQo0ePJjMzk+HDh5OSkoK1tTUTJ06kevXqZVE8IYSoMCReCiHEvZF4KYR4GJRJD3pYWBg5OTksXbqU4cOHM2HCBMNjaWlpTJ48mRkzZrB8+XK8vb1JTk5m2bJlBAUFsXDhQp577jlmzZpVFkUTQogKReKlEELcG4mXQoiHQZn0oB8+fJj27dsDEBISwsmTJw2PHT16lMDAQCZOnMjly5fp1asX7u7uvPLKK+Tl5QEQGxuLi4vLXV8nOzubiIiIsngLQoiHVHZ2drm+nsRLIcSDrDxjZnnFS5CYKYQoffcaL8skQU9LS8PJyclwbGlpiVarxcrKiuTkZPbv38+qVatwcHBgwIABhISEUKdOHSwtLXn55ZeJiopi7ty5d32dkJCQsii+EEKUG4mXQghxb8orXoLETCGE+ZTJEHcnJyfS09MNxzqdDisr1Rbg5uZGkyZNqFq1Ko6OjjRv3tyohfLXX39l4cKFvPXWW2VRNCGEqFAkXgohxL2ReCmEeBiUSYIeGhrKjh07ADh27BiBgYGGx4KCgoiKiiIpKQmtVkt4eDh169Zl5syZrFq1CgBHR0csLS3LomhCCFGhSLwUQoh7I/FSCPEwsNDr9frSvmnBKptRUVHo9XrGjx/Pjh078PX1pVOnTqxdu5bZs2cD0LVrV4YMGUJiYiIjRowgJyeHvLw8hg8fTrNmzUq7aEIIUaFIvBRCiHsj8VII8TAokwRdCCGEEEIIIYQQ96dMhrgLIYQQQgghhBDi/kiCLoQQQgghhBBCVACSoJcynU7HZ599Rp8+fRg4cCCXLl0yenzZsmX06NGD3r17s3XrVqPH5s2bx1dffVWexa1U7vbZAyQlJfHUU0+V+17XldXdPvN58+bRq1cvevXqxbRp08xUSlFR5OXlMWrUKPr27Uu/fv2Iiooyd5EqhJkzZ9KnTx969OjB8uXLzV2cCiEnJ4fhw4fTu3dvXnvtNS5evGjuIplVeHg4AwcOBCAiIoL+/fszcOBABg0aRGJioplLJx4ECxYsMHcRHmh3q+/MmTOHHj168NJLL7Fp0yYzlfLhUDQeVlaSoJeysLAwcnJyWLp0KcOHD2fChAmGxxISEpg/fz5Llixh9uzZfPPNN+Tk5JCVlcXw4cNZtGiRGUv+4LvTZw+wc+dOXnvtNRISEsxUwsrnTp/55cuXWb16NUuWLGHZsmXs2rWLM2fOmLG0wtwKGiWXLFnCu+++y5QpU8xcIvPbv38/R48eZfHixcyfP5/r16+bu0gVwrJly3BwcGDZsmV88sknfPHFF+YuktnMmjWLTz75xNCw/OWXX/Lpp58yf/58unTpwqxZs8xcQvEg+PHHH81dhAfaneo7KSkp/PrrryxZsoQ5c+Ywfvx4M5a0ciseDysrK3MXoLI5fPgw7du3ByAkJISTJ08aHjt+/DhNmzbFxsYGGxsbfH19OXPmDH5+frz44ou0bduW8+fPm6voD7w7ffYAGo2GuXPn8tJLL5mjeJXSnT5zLy8vfv75Z8OWNlqtFltbW7OUU1QMnTt35vHHHwcgNjYWFxcX8xaoAti1axeBgYG88cYbpKWl8eGHH5q7SBXCuXPneOyxxwDw9/cnOjrazCUyH19fX6ZOnWr4t/HNN99QrVo1QI1Kkbj6cLlw4QKjRo3CysoKnU7H5MmTmTZtGtHR0fj4+HDixAk2btzIyJEjuXnzJjdv3qRDhw7cunWLMWPGMGbMGHO/hQfSneo79vb21KxZk8zMTDIzM7GwsDBXMSu94vGwspIEvZSlpaXh5ORkOLa0tESr1WJlZUVaWhrOzs6GxxwdHUlLS8PV1ZV27dqxYsUKcxS50rjTZw/Qtm1bcxWt0rrTZ25tbY27uzt6vZ5JkybRqFEj6tSpY8bSiorAysqKESNGsGnTJr7//ntzF8fskpOTiY2NZcaMGVy5coX//e9/rF+//qGv4DVs2JCtW7fSuXNnwsPDiYuLIy8v76Hcw/qpp57iypUrhuOC5PzIkSMsWLCAhQsXmqtowgz27NlDcHAwH3zwAYcOHWLlypXk5OSwbNkyLl++zNNPP224tlWrVrzyyiuAGuIuyfnfd7c6Zo0aNejevTt5eXm8/vrr5ipmpVc8HlZWMsS9lDk5OZGenm441ul0hj/e4o+lp6cbJezin7nTZy/Kxt0+8+zsbN5//33S09MZPXq0OYooKqCJEyeyYcMGPv30UzIyMsxdHLNyc3OjXbt22NjY4O/vj62tLUlJSeYultm99NJLODk50b9/fzZt2kRQUNBDmZzfzrp16xg9ejQ//fQT7u7u5i6OKEc9e/bExcWFwYMHGxpngoODAfDx8cHb29twrTSKl5471Xd27NhBfHw8mzdvZtu2bYSFhXH8+HFzFVVUApKgl7LQ0FB27NgBwLFjxwgMDDQ8FhwczOHDh8nOziY1NZXo6Gijx8U/c6fPXpSNO33mer2eoUOHUr9+fcaOHSuVa8GqVauYOXMmoIYEWlhYoNE83F9DzZo1Y+fOnej1euLi4sjMzMTNzc3cxTK7EydO0Lp1axYvXkzXrl3x8fExd5EqjD/++IMFCxYwf/58+VweQps3b6ZZs2b88ssvdO3alRUrVnDkyBEAEhMTiYuLM1xbdCSOXq8v97JWJneq77i6umJnZ4eNjQ22trY4OzuTkpJirqKKSkC6F0tZly5d2L17N3379kWv1zN+/Hjmzp2Lr68vnTp1YuDAgfTv3x+9Xs97770nc8dK0d0+e1H67vSZ63Q6Dhw4QE5ODjt37gRg2LBhNG3a1MylFuby5JNPMmrUKAYMGIBWq+Wjjz7Czs7O3MUyq44dO3Lw4EF69uyJXq/ns88+k8YswM/Pj++++44ZM2bg7OzMl19+ae4iVQh5eXl8+eWX1KhRg7feeguAFi1a8Pbbb5u5ZKK8NG7cmBEjRvDjjz+i0+mYOnUqa9asoW/fvlSvXv22IwcDAgJ4//33Zbegv+ludcw9e/bQu3dvNBoNoaGhMq1S/CMWemlSE0IIIYQQ4oHXtm1bdu/ebe5iCCH+gYd7bKEQQgghhBBCCFFBSA+6EEIIIYQQQghRAUgPuhBCCCGEEEIIUQFIgi6EEEIIIYQQQlQAkqALIYQQQgghhBAVgCTootytWLGiTLb5ePPNN0v9nndy8OBBzpw5U66vKYQQ5Wnx4sVMnTr1vp+3dOlScnNz2b9/P++9914ZlEwIIYSonCRBF5XGtGnTyvX1fv/9d+Lj48v1NYUQ4kEwc+ZMdDqduYshhBClJjw8nC5duvD111/f1/MiIyM5ePBgGZVKVEZW5i6AeHjNnz+fNWvWYGFhQbdu3Xj55ZeJiopiwoQJ5OXlkZyczJgxYwgNDaVjx474+/sTEBBASkoKNjY2XL16lfj4eCZMmEBQUJBh78+BAwfSoEEDzp49S1paGt999x3e3t5Mnz6dsLAw3N3dyczM5J133qFly5Ymy/bMM89Qu3ZtrK2tGTFiBGPGjCE7O5uEhATeffddvLy82LlzJ6dOnaJu3bqEh4czb948NBoNzZo14/333y/nT1MI8bBYsWIFW7duJSsri4SEBF5++WU2b97M2bNn+fDDD7l+/TobN24kMzOTKlWqMG3aNJYvX87hw4f55ptvGDFiBMHBwQwYMMDk/Q8dOsT48eNxcXHB0tKSkJAQwHTMHjlyJHq9nmvXrpGRkcHEiRM5cuQICQkJvPfee/z73//m0qVLDB48mKSkJDp27Mhbb71Vnh+XEEKUip07d/Lyyy8zcODA+3rexo0b8fT0pEWLFmVUMlHZSIIuzOLy5cscPnyYRYsWAfDqq6/Srl07zp07x4gRI6hfvz5//vknK1asIDQ0lGvXrrFixQqqVKnCyJEjqVmzJmPHjmXZsmUsXbqUsWPHGt0/ODiYjz/+mClTprB27Voee+wxdu7cyW+//UZubi7PPvvsHcuXkZHB0KFDadSoEXv27OHVV1+lZcuWHDlyhKlTpzJ37lzat29Pt27dcHBwYOrUqfz+++/Y29vzwQcfsHv3btq2bVtmn58Q4uGWnp7OnDlzWLt2LfPmzWPZsmXs37+fefPm0bhxY0OD4aBBgzhx4gQDBgxg9+7djBw5ktzc3Nsm5wCff/4533//PXXq1GH06NEAnDt3jnXr1pWI2QA+Pj5MnDiR7du3M3nyZGbMmMGPP/7IlClTOHbsGNnZ2fzwww/k5eXx+OOPS4IuhCgTZdl4efz4cVasWIG1tTVeXl64uroyZcoULC0t8fHxYezYsWRnZ/Pxxx+TmppKfHw8/fv3p1OnTqxcuRJra2uCgoJ49913+euvv7C1teWrr77C398fb29vvvrqK6ytrenduzc1a9Ysce8rV64watQorKys0Ol0fP3119SoUcMMn7IoD5KgC7M4efIkWq2WV155BYBbt25x6dIlqlWrxg8//ICdnR3p6ek4OTkBUKVKFapUqWJ4fsOGDQHw8vLiyJEjJe7fqFEjw+OJiYlER0fTpEkTLC0tsbS0pHHjxnctY506dQCoWrUqP/74I7/99hsWFhZotVqj62JiYkhKSmLIkCGAqjjHxMRIgi6EKDMFMdDZ2ZmAgAAsLCxwdXUlNzcXa2trhg0bhoODA9evXzfErCFDhtCnTx9WrFhxx3snJiYa4l9oaCgxMTFERUURGxtbImYDtGrVCoCmTZsyfvz4EverV68eNjY2AFhZSbVDCFF2yqrxMjg4mBdffBFPT086d+5M165dWbRoER4eHnz77besXLmSoKAgunfvzpNPPklcXBwDBw6kf//+hucFBwffttzZ2dksX74cvV5v8t65ubkEBwfzwQcfcOjQIVJTUyVBr8Tkm1KYRYMGDcjKyuLnn3/GwsKCefPmUb9+fd544w2++uorAgIC+P7777l69SoAGo3xcgkWFhb39Xp169Zl/vz56HQ6tFotp0+fvutzCl7zu+++o1evXnTo0IHff/+dlStXGsqg1+upVasWNWrUYM6cOVhbW7NixQpD5VkIIcrC7WJgbm4uYWFhLF++nMzMTHr06IFerycnJ4fx48czduxYPv/8cxYsWGBImourXr060dHRBAQEcOLECVxdXfH396du3bolYvaGDRs4deoUzZs358iRI9SrV89QvoI56Pcbr4UQ4u8qy8bLAklJScTHx/Puu+8CkJWVRZs2bejQoQO//PILGzduxMnJqUSHTnF6vd7we0Gj6O3uPXToUGbNmsXgwYNxdnaWxTcrOUnQhVnUqVMHNzc3+vXrR05ODsHBwVSvXp3nnnuOd955BxcXF7y8vEhOTi6V16tfvz4dOnSgd+/eVKlSBWtr63vuyenatSuTJk3ip59+MirTI488wldffcW3337LK6+8wsCBA8nLy8Pb25unn366VMothBD3w8rKCnt7e/r27QuoEUDx8fF89dVXPP744/Tp04f4+Hi+/vprRo0aZfIeY8eO5cMPP8TJyQlHR0dcXV1p0KABrVu3LhGzAXbs2MHmzZvR6XT83//9HwDNmzdnyJAhvPHGG+XzxoUQgrJtvCxQpUoVvLy8+OGHH3B2dmbz5s04ODgwZ84cQkJC6N+/P/v27WP79u2GMhU0WNrY2BAfH0+tWrU4c+YMAQEBQGGn0O3uvXnzZpo1a8abb77JmjVr+Pnnnw3xVlQ+FvqizTdCVFI3btxg/fr1DBgwgJycHLp3784vv/xCzZo1zV00IYR4YI0cOZJu3brx2GOPmbsoQoiH3IoVKzh//jzvv/8+O3bsYN26dUyYMIGIiAgmT56MVqslJycHUIlyz549OXnyJG5ubgwdOpSpU6eSlpZ228bLqVOn4unpSb9+/di1axfTp09Hr9fj6OjIpEmTOHv2LOPGjcPNzQ1nZ2fOnj3LunXr2LNnD5MmTeKzzz7jypUrzJ49G29vb5ydnWnfvj3e3t4sWbKEKVOmAJi8d3p6OiNGjMDa2hqdTseoUaMICgoqt89WlC9J0MVDQafT8fHHHxMVFYWFhQVPPvkkrVq1YvLkySWuffrpp+nfv78ZSimEEOUjNjaWESNGlDjfokUL3n777Xu+jyToQgghROmSBF0IIYQQQgjx0Cutxksh/glJ0IUQQgghhBBCiApAc/dLhBBCCCGEEEIIUdYkQRdCCCGEEEIIISoASdCFEEIIIYQQQogKQBJ0IYQQQgghhBCiApAEXQghhBBCCCGEqAAkQRdCCCGEEEIIISoAq7K4qU6nY8yYMURGRmJjY8O4cePw8/MzPL59+3amT5+OXq8nKCiI0aNHc+vWLT744APS0tJwc3Nj3LhxeHh4lEXxhBCiwpB4KYQQ905iphCisiuTHvSwsDBycnJYunQpw4cPZ8KECYbH0tLSmDx5MjNmzGD58uV4e3uTnJzMzJkzadasGYsXL2bgwIF88803ZVE0IYSoUCReCiHEvZOYKYSo7MqkB/3w4cO0b98egJCQEE6ePGl47OjRowQGBjJx4kQuX75Mr169cHd359y5c7z33nsAhIaGMnbs2Lu+zpEjR7CwsCiLtyCEeEhZWFgQEhJSbq8n8VII8SCTmCmEEPfmXuNlmSToaWlpODk5GY4tLS3RarVYWVmRnJzM/v37WbVqFQ4ODgwYMICQkBAaNmzIli1baNSoEVu2bCErK+uur2Nvb0/Dhg3L4i0IIR5SERER5fp6Ei+FEA8yiZlCCHFv7jVelskQdycnJ9LT0w3HOp0OKyvVFuDm5kaTJk2oWrUqjo6ONG/enIiICIYMGcLVq1cZMGAAV65cwcvLqyyKJoQQFYrESyGEuHcSM4UQlV2ZJOihoaHs2LEDgGPHjhEYGGh4LCgoiKioKJKSktBqtYSHh1O3bl0OHTpEr169WLhwIX5+foSGhpZF0YQQokKReCmEEPdOYqYQorIrkyHuXbp0Yffu3fTt2xe9Xs/48eOZO3cuvr6+dOrUieHDhzN48GAAunbtSmBgILa2towYMQKAatWqMX78+LIomhBCVCgSL4UQ4t5JzBRCVHYWer1eb+5C/F0REREyP0gIUaoqa1yprO9LCGFelTW2VNb3JYQwn3uNK2UyxF0IIYQQQgghhBD3RxJ0IYQQQgghhBCiApAEXQghhBBCCCGEqAAkQRdCCCGEEEIIISqAMlnFXZS93DwdY/88zcqjV6nqbMsn3RvSqWF1cxdLCCGEeLDlaWHjx3BsETh6Qpex0PBZc5dKCLM6ceUWH608wdn4VDrWr8aEHsG4Olibu1hCVErSg/6Amrv7AvP3XSItW8uFxHSGLjxCcnqOuYslHmTJF2HTZ7D+I0iINHdphBDCPA7Nhv0zIDsFks7Db69BWvztrz/xG6x+Cw7OVsm9EJWMTqfnfwsPc+LqLbJydfx18jrj10WYu1jCXHQ6ODJfxb1ji+DB3RCswpIe9AfUwYvJRsfZWh0nrt7iscCqZiqReKClJ8KsJyDjhjo+8gv8dxe41zFvuYQQorzF7DU+zsuBq4eh/tMlr90xGbaMyz/4FWKPwvPTyryIQpSnaylZXEnONDp38FKSmUojzG7jx7DvB/X7kV9Vp06Xz81bpkpGetAfUKG+VYyObaw0BNV0MVNpRLlJS4Dd38OubyH1eund9/Qfhck5QE4anFheevcXwoyi4lKZsimKxQdiyMrNM3dxREVX61HjY4011Agxfe2hecbH4UsgN6tMiiWEuXi52FHT1c7oXFOfKre5WpS7q0dgy5dwbDHk5Zb96x2ed+dj8Y9JD/oD6rV2tbmYmG6Yg/7pMw3xcLI1d7FEWcpIgpmPQWqsOt47XfVyO+evPZCeCNmpf6/X28615DlbafARD76DF5PoP2sfuXlqCN7Ko1dZ9nprM5dKVGiP/gcSoyB8MTh4wpNjwaWGeizpvIqNjp7q2M4FUoo818YBNFK1EpWLpcaCaQNCGfW7moP+eP1qfNy9obmLJQDOrIOlA0CvU8eR66DP/LJ9TVtnyM0oPLarYPXFnAy4GQOe9UBjae7S/C3yLVKK9p+/werwWLxc7BjY2g83B5syey1bK0sm9gxmYs9go/NnrqdwJSmT1gEeONqa93/vufhU5uy+SI5Wx79a+RHi42bW8jzwTq0oTM4B0uNVL3ebNyHsc9jzPei0ULs99FusAui9avAMeDdTwzgBPOvDI31Lt/xCmMGvey8ZknOAAxeSOH7lJsG13Dh+5Sbz917CylLDq21rE1j9Pv5mxIMvNxMOzYGEMxD4NDTops5bWsOz36qfAlkpsLgfXNqlEvC270KnT6Hjx7D83yr2AnT8BCzv8N2beh2uHIKaTcHVu+zem3igbY6I449jsXi52jG4XR2qudjd/UmlbGtkPBtPXae2hyP/auVHqG8VNrz3WLmXQ9zF/h8Lk3OAiNVw8zK4+ZTda3b6DP54E9CDhQae+PTv3SclVvX+ezcrbAT9p86sg1X/haxb4OoD/ZaAV+PSuXc5kgS9lGyLjOfVeQcN6ySsO3mdtW+1Q6OxKLcyfLn2NLN2XgDA3dGGpUNaUa+UK5zaPB0HLiThYm9NY28Tva754lOzePGHPaRmqUrL6mOxrHm7nVSAi4rZryp7NUMhoOPdrzfVK6OxgusnYNc3hecu7oQDs6D9MLVwR/RmNT+obmeoWt/0va3t4LWNcH6rGh5VtzNYlV0DkxDlxcpEDLbSaDgXn0bPGXvJ0aqKzZrwWMKGd6C6GSrCAsi8qabaaCyh0fPGDYxJF+BGNPi2vL+Gx7tZ/ipE/aV+P/IrPPMtNH/V9LUHZqp4DSoZ3/kVNH4JGj4Dbx9T89ZrPHL7GAtwerVacE6Xq2L3izOhSc/Sez8FEqIgci1UqQ0Nnr1zg4EoVwmp2aw/eQ0Xe2u6NvbC1qpk797GU9cZMv+w4TjsdBybhnXAMj+WHYlJRpunp7lflTKrY648eoX3loYbjrdHJbDoP63K5LXEP1SibmhR9r3GTf8Fvq1Vp06tFqZHbp4Lg2vhUKcD1Gpe8vGTv8OKISqeaqzhpZ8h6IV/Vi5dHqx5VyXnALcuw4ZR8O8//9l9zUCidilZaIQHhAAAIABJREFUevCy0SKGEddSOHr5Js38qjB71wVmbo8G4PUOAQxqd/shyCuOXGHenovYWVkytGMAj9evdk+vH3szk593XTAcJ6XnMH3rOb7t2/TvvSETEtOy6T1jL+cT0wF4PqQm393m/ptOxxmSc4CcPB1/hscy/Mk7VF4eJgdnw9phhcePj4LHR975OUE9YPd3aoglgKsvBPeGCztKXntD/XvjrxGqYgmw8VPou1AtdKTTwd6pEPEnVKkDT3ysKnP1uph+bb0eLMqvsUmI0vJa2zqsP3mdzPy55080qEajmi58GxZlSM4BUrO1bDh1nZdb1wbg+JWbfL/5HCmZufRp4cNLzWqZo/gPh4wkmNkBbsWo453fwOvbVTK+61sIGwPowc4NXl6lep9z0mHNMDizRsWwbpPB7y5TF4rGsdTrhcl5gcNzb5+g3zhv4tw5qN5I9VQV7a2KOwWbx6ohlkEvQvv3QaOBsNEqOQdVKd34aekn6Bd2wPweha/T6Hno/Wvpvob4Wy7dSOf56bu5maH+3zT1deO3/7YxJN4Ffj9yxej4fGI6R2KSCfFx47V5B9l5NhGAR2q5sug/rcpktOTi/ZeNjvdE3+DSjXT8PBxL/bXu5M/wWCZviORWZi79HvXlw6fql2vH1wOhzdtwYWfh33zIAHCpqeqBmz5TcapBd1XPtLzDtnhxp2DbBLVjxSN9bx8LC3gEqB9Twj437jh6frpK6ovaNLpw5JEuV5X1nybo2amQFmd8zlTsfgBIgl5KnO1KfpSu9lbsP3+DL9acNpz7Ys1pgmq60Mrfo8T1e6ITGbassMXy6K/JhA3rgJ+HI0dikvlxWzRZuXkMbOXHk0FeRs9NzsgpscvBjSLbrul0epIycvC8zTx1bZ6OpPScOw6j+mXPRUNyDvDHsVhebl2bZn4lFwrxcCz5Oh6OD1iPbE6GaoW0KoO5/bu+NT7eMxUe+6Cw1dNUQmznAq/vgFOr1HCmoBfU3PE6j4GNM+SkFl7b8BlV6T34c+E5fR7smqIS9L3TVDAEuHIQYo/AGwdVJbKoCztVa2TSeajfTQVZ+yJTFVJi4ehCVZ6mA8C1giQxORmqbO7+Jd+TKHXp2VqWH7rM9ZRsngmuccfRNX9HWraWvDz939pzt0ktV8KGd2Djqet4udjRpZFas8HUmh0F8fFmRg4DZu0nNVtVHg5cTMLV3prO+c+9nZSsXKw1GuxtHsw5b/+ITqcWl/w7cxHDlxQm5wBJ0XBqpUout44H8r/csm6q4wHLYdv/wfEl6nzcCVj6Lxh22nS8jtkHq99W88oDn4IXfgQrO7C0USu0FyhYi+PqEdj5tXo/zV5VsbZBdwhfVHitrYuKvcVpc2DBS5B6TR3HnwZrBzUVKf2G8bUZN0o+/37pdMYxbs+0woo6qFEJSedVLLyTpAvg4G56PRJxV7cycpkSFsXJq7doE+DBG0/ULdE7vmDfJUNyDnA05ia7zyWW2H3HVGzycLQh7HScITkHCL9yixVHrzKwlR8AmTl5ZObm4V4KdS0Xe+M6raXGAgebwnN6vR6LUmy0/2ZjJHN2X8RSY8FbT9RlcHt/Lidl8O7SY+Tp1N//jO3R+Fd1pHfz+xu6felGOlUcbXCxq6R7tgd0hDf2Q9QGlTDX7aLiwsKehR06CWdUT3vHj0zfIycdfnm2MCZd3gc2ThDc6+6vr9Op+mdB7NBmw74fja/Z9W3JBD090fj4bvEwLUHFqKKjA64eUTHOtZZqmLB3A792haOdQMXuB5DUXEvJkMcCqFKk8tgj1Ju61Zw5eLHkNhQHL5jemmJbZILRcW6enh1nE4lLyWLArP1syg/Ory84zKFi921Uw4XG3sYVo175QezQxSTaT9pK83FhPDllO+fi04yu2x6VQOsJW3h0/Ga6fruDmBsZmJKYln1P5wA6N6xG+3qehuPG3i70vM+gaja6PFjzHkzwhYl1YPvksn/NgtaV1Ovw6/PweRX4sa3asqcoW2cIHQjN/l0YDB3c4eU/1BxKn1aq8ln/6fx7Fmu1KWitPLPG+PyNcyqAF6XNhuWvqMf0OvUcw3ZCqOA6swNsHQfbxsOM9pBy7d7erzYbIv+C89tLf//M03/A1w1gWjOY1hwSz5Xu/YURvV7Pv2bvZ8yfp5mxPZoXpu9m3/lSSDzy/d9fEYSO3UTouE28vzzcUFkrcOlGOn+GxxJ7M/M2dwBvN3tebVuHp5vUwMpSfe31aOrNI7UKk5G2dT0Myfue6BuG5LzAhlO33zUhN0/HsGXHCPl8I02/2Mh3YWfv+30+0KI2wpRGMMEH5na/857hpuhM7B2u06phinnFvmMK7n2p2FZoGYmQaOJzz9OqOJYYCeghaj1s+lRV5Nq9V3idlT10GKni2i/Pqnh3fpuaX35+u2r0fH66irH1u6mYa29iXZXrJwqT8wJR69V/mw4wPl/8+H5kJsPi/vCFB0xtphpTb+dOMTb9BszqBN+HwFeBsPeHv1+mh9jbS44yb89FDl1K5vst5xi/tuQe4UXXwiig1elKnPvvYwF4Feks+XdrP/yrOpmug6Wqc7N2nCf0i02EfrGJwb8cIjOn5G4VV29msjo89rZ1vKLe6FgXxyINjYPa1aGqsy2HLibR+Zvt+H+0jtfmHSSpSEfQ3xV2Oo7vt5wjLVvLrcxcxq2N4PClZI7EJJeI97erP5uSmJbN89N20WHyNlqMC2Pu7gt3f9KDyiMAWg9VDZAajaq3JRXrOY7acPvnx+wtmSCfuYdh4ee3wbdNVH159pOqYwQoUfcscYyJePivktcAJF9U9cuv6sKUxhC9VZ2P3go/d4bd38K691XDKECveRD6shpp1W4YdBl79/dRAT1UPeg7zyaw9UwC9b2c6BFaC2vL0mufqFvNie0fdmRHVAJeLnY0r+0OQFPfkr3Lps4V3KO4wGpObIuMNwzPBPVdu/7kdcNrAFhYWDD/tZbM2X2BK8mZdG9Sg86NqqPX63l/eThX8yuvUXFpjF59koWD1VwibZ6O95eHk5Af5M9cT2Xc2tP89HLJ+SIvhHiz9OBlCuJlNWdboyS8KCtLDfMHteRITDI5Wh2P1nb/x8OSYm5kkK3NK/V59SWc/F0tHASqJ2LrOPB/HHxalN5rtH1bBZQCrd9QrYJ/fagCHkDcSTVf8a0jdx9eXqsZ9F9ifM7RA4L7Fun1sYBWQ9Wv7v5weX/htVZ2JRfoSLqgKr1FXTlY+PuplWqhugKZSWrRurZv37ms6Ykwu0vhl0edDjBwZenMmdJmq56y7Pz5R0nRqjLeb/E/v7cwKfzKLY7G3DQca3V65u+7ZHKU0P3ad/4GM7cXVjJ+O3yFdnU9eaGpWlxryYEYRq08gV6v5ppP7deUp5uof8eZOXlExqVSr5qTySGgjrZWrBzaloMXk7Cy1BiNBKptYhhnbc/bD+38/fAVVhy5CkBWro4pYVE8Xr8qj/yDhTGj4lJZtD8GK40FA1v7lfvQ0nuWmwUrh6iEEVTPRdjn8ML0e79HcB+1yGV6fiO1izc0ekE1PhbvDXmkn/pvrRZw9VDhefsqpodb3rpcMmG+kv+8jh+pBTITo1RvuFM1CF+qes6LilgN/h1UBbJoJVKvV4t3Xj4Ivq1UT3uV2mBpa9ywUC1/tesnx6nYG7NPlb/F4Lt/Nno9HFsI0VugemNo+V+1UvzmL9Q8c1CV8eWvqBEErYeqdUcKGj0aPHP7YaighqEWfI7aLNj4iXofLjXvXjYBQEaOlu1Rxh0sf528zufPGy9MNaClL8sOXSYjP3muX92Z9vWMe88BfD0c2PbB4+y/kISXix31vVSd58kgLyatjzQ0HtpYaXj2kRqcT0hj/F8RhnaYsIg4ftl7kf92KPz/vvb4Nd5ZchStTo/GAia8FHzHnuimvlXYOeIJdp1LpI6HI01quaLN0/HmoqNcT1HbCG45E8//rYtgcq9HbnsfnU7Pn8djOXb5Jq38PXiq2OhPUHPqizsak0yXRtXRWEDRHP129WdTpm89R/gVVRfI1ur4cm0E3ZvUMMuCeyXodHD9uPo7c7q3qaz3xdW75MjKqg3Uf29eVj3cmckqntVumz/CxgKjRNq9SNxIPFtYL27+mlodPS9XzSEvGFJ+eb+KHz3nQMvX1ZTMAm1M1Au7TgTPQLh8QMXP5q+Zfi/rP1KfFaiFklf9D947pUaI6os0RMXsgWvHoUYwPDf1Xj6lCu2hSdAX7LvEJ6tOGo7DIuKZZSIJvZ24lCwOXEiiYQ0Xk4k0gIudNc8EG3+pta3ryQdP1WdGwRz0x/xpd5uk9sWm3uw6m8ifx2Ox0ljwSpvatPT3IM9E67evh0OJc1UcbUrM8c7K1XGxWGtpxLXCP9ikjBxDcl4gMi4VU1r6e7BgUEuWHbqMq701g9v7Gw15Ajhx5RbXU7JoW9cDBxurEvu1/x16vZ5/zznAjvyhXQ28nFk5tG3ZDSMtaJ0rKmZv6Sboj/4HqgfBxfxF4up1VuevHDK+Lum8Gqru4A6HZqs54+7+ajj8vVSgnp8GdTup3vF6T4JP/v6+HT9SQ4MSI1XPUdf/UxXcotz9wam68XwevzaFv5saSmptf/cyHZ5r3LJ7YbuqfN5u/vv9yLihhsEWdfnAP7+vuC1bq5INnXb5QztPxd7i+81nuZGWQ89mtej7qO993TvyeslYdCb/nF6vZ/KGSEOlVKvTM3ljJE83qcG+8zd4ff5hbmXm4mRrxdR+TenYoGQlSKOxoKWJhoRGNV34b4cAZu08T55OT8s67vy7TW0Alh28zPLDl3F3tOHtTvUIqulqKJNxOVP+doJ+6UY6L0zfbajI/3bkCpve60BV53ubbpOcnsOEv85w7PJNHq3jzodd6+NcVsM7b10uTM4LXA83fe3tOFdXW0aGL1ENdY/0UzEP1LoZe6erWFW/W+HuEh0/UpW1iDUqKX7mG9Pxx81XreR7q8icWt8ic9VrBKufAlVql7yHqXOg5sbvzp+utP9HiB8JHUdB969UpTInVe2p/tiH6hqNpYr9j/7H+D7aHDV33MZBla1og+zOr2HLF+r3k7+rRZn6LjRuLAXVmJp8STUm/3cXnMlfJK7R86bLXqD4qAN9nmqclQT9ntlZWVLdxZa4lMK6lK97yTpaverOrH/nMf44dhVXB2tebOqNtaWG7VEJzNqhvhMHta9Dx/rVsLO2pEOxoe/VXez47X9tmLfnArl5ev7Vyo+61ZxZf/J6iUESxWPnpA1n0OZnujo9TFofaUjQk9Nz2BN9g9qeDgTVLBxV5O5ow3OPFP47uHYry5CcFzh6udj3bTHj1kYwJ7/neu7ui3zwVH3e6FjX6JoWddxhW7TRuUfruOPn4cjEl4KZtCGSlPw56H1alGxU0Ov1LNh3iQ2n4vDzcODtTvWo7mLH+YR0o+u0Oj0xSRkmE/QlB2JYfCAGF3tr3u5UjxZFOsBK3c0Y+PUF1YGgsYJOo+/esXG/bBzhue/UaNCsW+AVrHadyMlQPd0FOwIdXwKvrFPrd3T8GLZPVB1T3s2hzVvqmltX1Sibgo6PowtgaP4IpuLzva/lJ9JdxoJf28JF4nxbqvMJkapB1K+tivEtX1c/dxJ30vg49Zqq61mZaGi5lzroP3UtHLZPUmUIGaBGtZaBhyZBn7Ipyuh40+k4ktNzqHIPc3W2RcYzZP5hw4JCnz7T6I4LvRX3Rse6JQKSKdaWGr7v15TPnm2EtUZjmG/Z2t+Dvi18WHpILUTXrq4nvZr5kJWbx5SwKHZGJdKghjMjuzYoEXjsbSxp7leFQ5cKK1BFg341Zzsa1XDh9LUUk49fu5XJ3ugbNPByoVFNF9rU9aRNXdMNDKNWHGfxAVUJqupsy/LXW9+x1+lerTx21ZCcg6qgf7Mpko+7N/rH9zZJm3Vv5/4pvzbGCS+oytnJ3wqPPeurnvB9M2D9CHXu/DbVA/O/PXfvWddYml6EyM1XzVlKiARnr8Khmlm31Dwfz7pqFfc+C2DtcLXYSINuxvOXgl5U8x0TI9Wxuz80yZ+vtHe6WknexhE6jIBGzxU+L9PEF7qpc3+HxkQSUrw3TNyz+JQs8vR6arje/kuvYQ0XngqqzoZT6ovaydaKwe3rkJatZcDP+w1zLg9dSsbJzqpEI+adtKvniaXGwmiYY0F80ukxWogSICVTvdaY1ae4lf97WraWz1afZGeDJwrfV2oWu88lUsfT6bbbP458ugGD2tUhPVtriGPrTlzjw9+PG67ZfyGJXSOe4LFAT+btuWg4b6WxoE2A6ThZlDZPx+QNkawOj6Wmmz0fdWtAMz93Vh+LNSTnADczcll/8hoD8xewu5thy46xNX/KVGRcKilZubdd0PMfc/dX87Gzi2wGXtBTcz+cvaDduyXP27upRSyLs3NRi58VXa8jPRFWv1XY2/zsd2p7nd6/qDiWEKmGgHYec/ty+LZUPdsHZwN61YPf7DaLJRVd4wPgwE8qQQ99GRr3VA0Xd9tKLf0GzHlS9YKD2kGj//LCeeXHFhpff2atipd+bQp7lQCcvApXUq7WsLDX/m6Kr4pvoTFusBB3pdFY8MXzjRm2LJy0bC2eTrZ8+ozp+omvhwNvdapnOI6KS2XQvIOG5Hnv+RusfbsdDbxMr+VQ38uZ/+th/P+nZR137K0tjUZaFk/uC2JjgdSsXHQ6Pcev3uJfP+8nLb9X/n+PBzCiq+m/35pu9ni72RtGZAJGiezxKzeJTkijbYAn1Vzs0ObpWLj/ktE9ft17sUR9uGP9anzYtT5zdl3ASqPhzSfqElxLxeVezX3o1dznjnPe5+25yOd/qrWedp2DQxeTWf9uezo3qm40sqGasy1NapVcY2HT6ThGrjhhOD50MZmdIzreds2mf2z7RJWcgxrpsulT9XdYdFG2jCTV2XC3tSPupPFLUL+7GplUsIjlmbXG2/XqdRC+WCXoHT5QvdiZyaoOWODUysLkHFSsP7USWr8JHvXgRpFGPv/HC38PfEr9FNgxuXCapI0zDFxR2GkEanrp1i/h+DLVOdTlc6jdTnUyFfTeA1RvokYdtHlTTZXMzW+ICeqhevZLU+I52DJWDbNv9Dw0HwS/PFfYERSzV30X3a0h9G94aBL0tOySc9yS0nOIScog8noqbep6UKtKyRZPgK83Gq/2O2VTFANb+WFTpOfoVkYuienZBFQ13bt+P4oHBQsLCya8FMxbneqRlZtneI3P/zzF3N0XATh9LYWLiemsGNq2xP2m9m/K2D9Pc/yKWrzkk2JfHDMHNmPc2tNEXk+lQ2BVRj6tvti3RcYz5NfD5OSp926q5bPAufg0Q3IOaiuRmTvO8389mtz3+9fm6dgTfQONhQVtAjzYXmxuPqi9jMuMZ2DJc8n5XzLabLXA2pXDqoLU8nXTq2JePawSaa9HCnvH78XTk1RjQPQW8Gqitv0BNYyyqPjTqle8WkM15+fSHtVCWtVE2W/HwgKqFfkiPvgzbPgEtJnqtfsvV8Hzv7eZ22jrDEO2QeQ6FeQbdFcJ+Zl1sKFIIr/8FXjrUOEXzSP9VPJeMATUqbpxEL+ds5vU6AavJmr1+oIh8ddPwsaPVa987fYln1fKU9wfBjqdnpErjrP88BX0eugeXIPv+oQY5m8X9+OAZmyPSuB6ShadGlajmrMdW87EGS2IBLDhVBzPBNck/PJNRvx+nLPxaTweWJVJPYNNLowUUNWJHweEMn1bNDlaHa+2rU3rANXjbamxoGfzWizaX7i4WN8Wqof+arLxfPTYm1nodHo0GgsOXUxi4OwDhsrs64/5M6pbYTJzPiEND0dbXB2sqepsa9Rrvf6k8Tz0mxm57D9/g04Nq/PF80H8uvcSDjaWvN2pHj75PWinY1NYHR6Lp5MNvVv4GC1U9POuC8zM7zm7diuLQb8cYu/ITrjYl4wpps6ZkqfTs63YcNvNEfc5J/x+ZN1SCwwVlX5DzYne8oVKmkP6Q/vhxg2KN6LVPG/XWtDwuTuvLnwnRe/51wgVj0AN2/7tVXjzoNpjd8g24+dl3lTbSrr6qilCRXX/Ws1dzEkvjKmZyeo9eQSo0U+genCKNgBaF6lD2Dion6KyU9U8UFtnlYhrLNWIohtF1sk4Fwbnt6jHARw8jUcc2Tqr133iE1WJj1ynytTt6zt/hjei1XdX5k3VgFCwtef5YiPG9Do1j75447G4I18PB/w8HDgdm0JgdSfDdo16vZ5955NIycqlQ2BV7KyNR/6FRcQZknNQf7+bI+Jp4OXCxcR0Zu08z638nSRMDYcHNXpy7qst+GZTFMnpOfRu7mOYBlSg76O+/Fikl7p3cx80Ggumbj5rVD+eteM8g9vVMcTj1Kxc4lKyCKjqhKXGghn/asYnq05wNj6NJxpUY+TTqg4xaf0Zfsi/v521hl9efZQWtd2xtdKQXaT+bG9teuTj0MfrMvTx23dkFSTnUXGprDx6FVd7a/q28MHNwYY1x42nsETGpXIuPo1/tfQlOzeP1eGx1HC1Y/iT9U1ua7c5wrgXODM3jz3RN4xGD5SqmzElz60drkY5unqr3tntk1RPtm9rtY93QSdKbqZKjjOS1FSUogvzZt5UI3Hc/Qv3/ba2M95hwsFEw7FjkX9Xjh7qpyhTC0fauarY23ehirsJZ9QoyM5jTL/nzJvG6znlpKqFPgeuLDx38Gc1YgjUiKdFfdW0nS75I4jOhamG16fGq+OaTeGtw2qND1cfCChshDcp8axKqGu1MG7AjPhTNR5os+HRIdBikDqvy4MFLxb+/7oWruJo8VGaEWsenARdp9MxZswYIiMjsbGxYdy4cfj5+Rke3759O9OnT0ev1xMUFMTo0aNJS0vjvffeIyMjAxsbGyZPnkzVqqaD0d/RqIaL0VAcO2sNK49eZdpW9cVobWnBTy83p6OJbc1SsowrmRk5WnLydIYEfc6uC0xcf4ZsrY6gmi7MfbUF1ZxLDr3I0+mZv/ciu6Nv0MTblf+097/tMO08nZ5DF5NwsLEytPh5uxn3Ym06bRxUjsTcJCk9p8QKnjVc7fnxX8UqIEX4uDswc2DJ4f7fhp01JOcA07ac47W2dUyW+WZGyYVCTJ27m7RsLb1n7DX06If6ujGgpR9/HIs1uu5eeqf+tqxbJc/dzE/Q/3y3cE535Fr1h9ttkvG1xxapOTIF2r6rWgILXD0MkevBoy407mFcqXL0UAGvOFcf4znjlrYqsY3aCEsHFK5E3HUCtMp/7bxctSe6jXPJ4fl5WtUQYJvfoJSeCOtHFd7n+gnYMQmemWL8PG02nN2ofq/3pKqAFu+hL5hDX0Cfpyq2BQm6V2MYtFENk7JxgBb/US2QGUmqtyg7DR7pY9xyvP8n+OuDwuOYvfDc9+p9LOoNKWr+L8cWqiFjRRedqnn7+XEVQUWMl9ui4ll2qHCrn7XHr/Fko+o8H2K6N1CjsSgxhNzUnOk6Hg7odHreWHSEK/lJ9OYz8Xy5NoJv+oQAcCszl6MxydT3cqaGqz1PBnmV2LWiwNjngmhYw4XwyzdpWcednvlboXVrUoOlhwobDLsGeRnWwJi65ZxRT9PsXRd4vUMAeTo9r847wMmrKdhYafjwqfoMbm/ce2FqRFDB+xzYunaJHu4jMcn0mbnXsDjU70eusuatdoZtlXafM17j4WZGLidjb9Ej1JtF+2MM041CfNzo2tj0Z1CcpcaCOh6ORjtuBNxmWlapyEkzngcIkJUMi/oU9mxs+ULFq4KhgDH7VC9EQSNdYFfov1T9fuuqGnZpZWc81L3geUkXVEXM2cSK+jH7jI8To1RjQfEKZ9xpmNetcGh+s1fh2WI7axTt+b56RC3eWTBKoP1w6PSZ6i1fOzz/Ios7b5WZcg1mPVHYe+XXVu3Nm2GisTmjyJSBTp/mf5YZ6jWe+FRVurGDl2aVfG5CJJz4TX1uIf1VRTo7FeY8VTjH//Qq+PcaqBlieuXkirIbhwkVMV4CvLP4mOHvdU/0DT794yQ/DWzGq/MOGhYA9nazZ+XQNkYjHeuYWu/Cw5H0bC09Z+w1LAy39sQ1Fv+nlWFtD71ez4ELSeTp9bSq40Erfw+WvX77bQY/eLI+dTwdOXAhiRAfN/rlTze6VaxnXavTk56dh4eTWvPjsz9OkpGTR0BVR+a+8ihNarnyx5vtjJ5zMyOHWTsLG5GycnVM3XKOBYNb8k7nQMNORhoLeKdzYQ9nalYuhy8lU6+6c4n6bXxqFjujEqnt6UAzPxUDTsXeoscPewwJ/7JDl1n/zmN4u9lzuMgoURsrDVWdbbGwsGBwe3+jOJ6n07M6/CpnrqfyeGA1Wgd4mOxYC6hahmt+NHqh5Pa4+jw1bSU3U/UiF4jZC/t+UKMXdTqY90zhmhHbJsB/NkPV+hB7DH59rrD+2vYd04uj+bZUr396lTquUlslpcVl3ixsFGj8EhycpRJUUJ1BjfMXZKtaX219WdS147B/hkpwH/2P2gM9N6Pkgp/FY1/xumNOqqovB3QsWRct4FLDeORBnlZ1aCVGqYWTCxpfjy3Or5fnN4Z1/0Yl4onnYNm/C7/D1g5Tn0ndTmpoffHGlLhTJcvwT0Y53EGZrOIeFhZGTk4OS5cuZfjw4UyYMMHwWFpaGpMnT2bGjBksX74cb29vkpOTWbFiBYGBgSxatIhu3boxe/bsUi3TN31C8HVXAcDRxpKJLwUbBZTcPD3fbza98m5Br0yBZx+piVP+okPxqVmMXxdhCBinYlOYtsX0qtFfbYxkzJ+n2XQ6jm82RTF8+TGT193KzOWZqbvo89M+np22iyG/HkJvYh66f7Gg4ulki4udFWfjUuk9Yy9NRm9g6MLDfytRhpKjDrK1eYaEfeXRKwxbdozZuy6Qrc2jqW8V6hWpBFpYQK/md/6ST83KZc3xWPYXWfF51dGrRsPtj8TcxFKDYXVlgOBarkZBvtSZGs5u76aC44mUyvJrAAAgAElEQVTlxucLtvkpqvgWavtnqIWUQLW0zeqkkt+VQ+D3e1gkCFRwds3/d6ixVq2UDu5qAbui2wRtHa/mM2YkwY9tYP6LMLuzaoks+Dd0dKFaDfP/asGSAf/P3neHR1WgX5/pyWTSe+8hhRBKIBBC7yBSFEGaiCAooii6yuqurnXtFddOkSId6VJCDT0JpPfee5kkM5n2/fHOvXfuzATXFfbn863nefLA3Exm7kwy733Lec+hKVFbOf9xAMvdRLUS+GYc2RntWkwK7moregXeVgpic8qkz0Da05z0OnV3NT3AdxNIYOT8P0mx01R9nfFyZ3BrO513Uz5XnDNwDAAkxourg98fXizkjxgvzXf3AKC44betCoS6K7BuYjgkIipGhwQ647GkENR3qtjinAEjEnSluBmJ75zBsk03kPTuWey6YTlpSK9oxaHbNWjt6oVYJMSS4YH4YF4c5sX7s1OWf8yKwdrxYRgW7IJVo0Pw7oPc3595XNPqDejR6LDxbBGyqin29Gr1eOd4HurN9i0fGxmMwQGUtIiFAjw9PqxPTRIA2HGtgqfcnFvbwWP/mO57ArTPH+augL2NBEeeTsIPy+Kxdfkw7Hsi0erkBwCOZNRg3AfnEP/maXx0qgAGgwHTY/nF/IzYf6+4/4/gFEAUd1OI5VxxzqA4mfv/lY38ZK3gBNCQR+JFX40kD/Ff/gp8M5aLMcdeoCLz4Grgs0EkzGYO80akSyi/wGeQ8gl/bz51ExX+feHC+3wKf8qntAo0dAWwOoWYTk9e4RoQeUeBg0/SNIg5/5vf86ml5SnECIqbz1/NsfMAIiZzt4NHkyDSgh3A0+lAgpVkmkHtbeDr0XR9OfES8MM0SliLk7niHKApeeYemvjbm4mDOvrT7/QPij9ivFSqtRbaPekVrbhS0sxz56lu68GPV/mU78kxXpg1kJvUzozzwdT+XrhY2MhTbTcYgJ9v0bVOrdVh/tdXMf+bq1j47TXM/jIF3b38uHa1pBkv7cvAB7/ko7FTDaFQgIfi/fHBvDgsHh7INgnNdUESQ10R4Cqn1SBjcQ4AxY1d+OBkvtXXr9LoLRTqGSG7x5KCcWRtEt6eE4uTz47BnEGUF6aWtyDxnWQs23QDo987ix+vlLE/m1reijHvncP6PbfxwL+u4LVDVBTtulHJm8aXNHbhUlEjnp0UwRb4EpEAL06NhJPc+vrqS/sy8Oyu2/j6fAke/vYqdt+oxOLhgRjXjxo2UpEQT48Ps4jNdxVDHwMiZ/KPCUSA72CO+m4KhmFTdoEvjNnbya3ZXHifP1y6/AW5AlnDQ1uAFcnA4n1kr2va7KzLAjYmAO8G0r/12TRIWZEMLNpLXyvPElvSGtoqgR+mUo6W8ROwaTrQWECaFuYTbnPVdi+zPFEoATx+4yrrgVXA/pX0fnw3gVx9AL5dJ8A1QUrPWTaYmWuVo7/lnrt3HBA9h7tt703CnfcA92SCnpqailGjiGo6cOBAZGVxC/7p6emIiIjAu+++i8rKSsybNw8uLi6IiIhASQkVzEqlEmLxr5+aWq1Gbq6llUVf+Oo+L9QptXCVi6DWtkJjMh0GgNbObquPN9YTwCgPpNV2I9RFhukRMvZ+uQ0qHj0JALLKG6w+zu7r/MB8IqsO6ZnZsDETWdqb1YZckyL1ZE49diSnYbAPny63MEqGwloxaju1UEiFWDPUCYUF+Vh1sBIV7dQVPZZZB1WXEi+M4k+3Wrq1+PhyI27V9iDcVYZnEt0R6MQPaBODZDxLttFBdqgpK8JHGa3Ykk7Jzf60alzMrsCLoz3w+lhX/JwrRnO3FmNDFPA2tCA31zoVvaZDg/XHa9Cmog/GqEA7/HWsJwpNuqAM8kqr8NxQR4z18UK3Vo+RAXYoLSqwuN/dgr1NBMxbCyW+c6HOz0eYzAmSHu6Cq5Y6o8Tsdx2iVsGUrKvX61GQlwuD2AYByR/AzjRI5BxEYeo5aOUUIIXqdujFcutUxck7YNOaD42dF3Q2rkBuLkI7GmH6WzP0diE/NwsuBT/Bo8nkPSo4jvJzW9HrGIKww2shYAJS3hE0/vx3NEUvQ5jcC5JuLqDXOcej1eS1ORX/DO8Gk+5hYy5qT32BttDZ/POUxsErZBacSo9AL5KhOXoZmtttgPZcCDVdEPZ2QmvHLxjsK8/Az5TG2atE0+mP0Rj3FAAgWCeAaZjUC8QoKCgE9DqEi+UQaTkhxFbnAWiNnwV54y10BE6GrkkPNP37ceK/jT9ivOxus2y8KNtakJubC63egAalFp4KMZvk9YUpvsDwBwOg7NXD10GCmvIi6A0GeCnEqFNyCWWYkxC5ubn4x7FqdBkTQp3egDcOZyNaroTY+DxfXm3C4XyKjXKJAO9O8UGYq/U9wen+wHTjfnllCddsGucvRqpJKI73tUVHbRmyyvmMJJ3egAtpuejvyb9AvzXOBVXtCiikIjjZ6pGbm4vGLi2qOzSIdJfx4nmPsgPmqKuuQG4vUc4n+uiR5ifHjapuONgIsXqoG+oqisF8Cr0BQAcU5NOkvatXD5EQ7HPUdWrw9AHOWeOzM4WQ9bZh+01+3P0xpRij3H+/HZI1SJTVCFPzX6eqvR4ygRACA3eNbRB6oNn49+fb2QnzDduS4iLYV5+Du2nh3FaO6uRv0e0Zj7Ab34H9a9N0ofP4a6ga9QHvMUShK+DTXAu7+utQO4ahdshfocozs48E4NdYBXMvkNLcW1A5KeGZ/gkcy45BJ3NGw8C16PQbi8DmavCuvnotinLSobH3ByAC7IYDzQCac+FYcgQ+Nzg7yq7Mo6gYtxEedZUwlyOsKs1Hp78vbMZ/BaeSQ9CLbdEa/hA0pTUAaszuHQLUq4B6eg8Ful7YNmVCY+cJjYKuWF43P4azaYO5IRsVF7ZBL7JBkNmjNaqEaMrLg93gl+Bz+W8QazrQa+OOqoS3oP4NedV/G/+teAn8tpgZ7CxFaSv3GYtwESOnsMzifmXVDcjN5eeeq+Ns8EAYFcrudmIU5Oehu9lyUCBQdyI3NxfnSpS4bmK1m1HVjq+Op2JaBH2q0mu68crpOjYuHEgtx9ez/KzG6xg58Np4T1yu6IavgwQzI+2Rm5uLqvZeng4GAORWNfX5fiT4yXGtirsOj/eXsPcVARjkAGiau5BrnMf845catojX6Q14+1gOYhVdkImFePdMHY/ltPVKGSb56tHVYclurK+phrfBFl/P9EJRsxoeCjGcbVXIzc1FXacGdUotoj1kkIqEUPbqsC+tivfz/0rOQ6xCib8Mt8fyWFvIxALYy/S/qbb4rZC1FiCw8AyYlqteZIPa+BfRUdsFocYVYVIHiHq5mFptPxAdubmQ15Uj0OyxWlpaUJ+bi4DmavBKZoMOxTnp6HUIAvQ6SLpqoLHzJoYhAEBOX4X8hkDQqZWwbTHGzMY89OxagbJJzP63MTMuoOupSNUKncyRdCuMcC7YBS/T5qxOjcbz36IpZjkEca/ARd4P0o4yKH1GotM+CTB5nwVuU+DjewX21RegkzqgYeDTaK9qAWBZR7jmbIJL/k+AUIym6EfRGv4gxD1NCMvax10nYEB38ocoF0QgXNXBK3j1aiXyc3Jgq7K3iI01Ohe0G8/LaeCz8Lz1CYTaHqicIlDjOh5BZ1Zw0+3OWtSe+RJtYXMtzvH34p4U6EqlEgoFN1UQiUTQarUQi8VobW3FtWvXcPDgQcjlcixatAgDBw6Es7MzUlJSMH36dLS3t2P7dis0XzPIZDJERf2bQigAypq6UN3RBIWdPYYFu2BmnoZHnV4xJgJRUYHoUGlwKrseChsxxkd6QCISoq+nCYvQ4/3LLaht54LpnGGhiIoy/xgBPs7NaO7mAoyTXIoBMdEWQVNQnAPzP0gbJ09ERfHLxigAkxMGoKy5Cz5OtrCRiGivvp3vfZjXrLV4nx7fehM3q2mKlduoxqfXO3D8Gf7+blQUMKhfHS4UNCLS2wHz4/0hFQuRfDiZd78LZUpsXDYSUTIxRgy28iZZwc6fs9jiHAAulndB5+CDxyYGYk/2BfbC4GAjxrLxcXh0yw1Wfd7DXobk58eyLIa7jhA/IOMzzprHMxYhibNJtEf/PtlK6HoBsS1k972PqAizP46eF4BDT7E3hQmPIzLWKNB03QHgrYcKEB4RCYiktKtdep7U1Ke9R3vWFjDb6W9aCZzh6POC2HmIjB0MVO22+MlANztArrHoFrpra+EeEwt4Gn3O2yqAmDnwSlwLL9P9zjZLdXtvFwW8rX04YrYCvd0QCUXwEMvgAdDU7MwbtOMekAg8vINTjheUApf5D+Hm4QM35rENf6f3x3juwtHPI7I/UaIh/go48hypGAeOhLNfGJxPEeXJK/tb6vgGWWoz9IV7eWG2hj9ivLzUWAKzP1QonFygtHXHmu1paOhUw9vRBv9aPIQVWVOqtTiZXQeJSIhJ0Z4Wu5am+M7RFxv2ZyK/vhNjI9zx7gMD4GwnhfIQf5ewS6NHcFgEFDIxatp6cKSAi23dGgNOlOuxMSkKxY1KvHkkB0WNSkyI9MRL0yJ5z683ZqpCoQBRUUBsRCNO5dQh2E2BhcMCYCsVYW6HHW4e4ISCPB1kmJUUx9MaYWD6Lm5OKcUbR3Oh0xvgLJdg6/IEdi3pOTclLlVcZmmkY/u5Y85ovljb7rgYKNVa2IiFfe746/QG/HV/JvamVUEsFODx0SFYP7kf8tKroDdU8u5bqZKhQ80vAFp69L/pWvmb0OMNnODbitl4RQAjV9MkXN0JRM6Ax8xX4cHsZMv/AmxN4Vg7YZMQMuI+4IJlMe3rFwD4+NLU1wT2UoH11zSYvH5tAPQp5SpYA/yUwt32GYTgxNlkPVREIp0ibTf8rv6dptcjVwE/P8ndPzAJYcMmwyouP8O7adeQiihPG2DC00DJQY6h5RQAv7GPGvfUo4AkWhXiFfGddbRu5DOYT9NvKQE2P2BkDwmAUc8R5b7UAzAbwAUEh1P8azwDZBqvC+6RcJ/+MtztXOlC72IDXNkIqWsYQmJHAPa/zfbpvxkz/1vxEvhtMfMbFz+8uC8DWdUdSAxzxXsPDoC9TIIf0s+hxpgfSkQCrJwUiyg/S2FK82eJigIu1glwIJ2m5mEeCqyfGQ9XhQxXW0oB8HUlpPYuiIoiZuFXt9N51mTVHRp02HggMdQNnSoNTufWw1YiwvhIT0jFlN8uM3v+SIMB4ZfbUGgypJk1JAhRUeE4m9+AT04XokutxeKEACwbGYwtYRHYeb0CRQ1KTIzytOqaYYquY+Z73wYEhITDxU4KwYU2AFyxrzcAfkEhWBcswPnyFDQpKW4MD3HB/LGD2PUlU1O7T08X4pMzJTAYKGfcsXI4ohxtIBZW8lY3Hexs711s7As/PAvouNcn1KngO+JB+DLMFbfDJCTX1QgMXAjf+OXwBYB+EUDhJqAmne4nVcBl8vNw8YgCep8gViYD/+EITZhGFPFdS4GOKlozmreZry+h0wIik1x6N58lYdtWaPn+NBUBu5cCDdnEWJz7DYnMAYBuAJDOv7t7cH+4M48xgCjnffqbxB4C1J0Qi23gI5LAqgpAwUkgk2NVeqV9AK/B0wH/SKLvmjCO5QoHOv+6VfSeGiEcthJR0dEAogFtMbGidBpg4EL4TF0HH0bjKCoKmLQG6GqEjUsIQvKOkjaACbzVxdbz4D7w78bLe1LhKBQKdHVxHRS9Xs92LJ2cnBAbG8vu/8THxyM3NxfHjh3DihUrsGDBAuTl5WHt2rU4fPjwXTun5Lx6PL41lZ12rx4TivcfjENCsCvy6zowtp8HxkV6oK5dhVkbL7F2GUMCnbHr8eF9Jk4So9/3x6cKUN3Wg5lxPlicYJ0etmFaJFZuvYmuXh0kIgFenh5ltaN5/0AfbL5cxp6rk1yCCVEeMBgM2Hm9EinFTejv44hHRwbBRiLiUd2d5RIEuspRbmKtZs3qJ9VsUp1b24EutdbCL3hKjJeFb6WTrRSV4CiqdlJxn57yBoMB314swcH0Gng52uD5yf0Q7eNgoSgK0K5/f19HHHhyJLZfK4dIKMCS4YE4lFHDs4Zr6FTj+T238dUd9up/F9K3831z6zNJnCJiMimXByaReq7PIOv0ycFLaC+n5BxRdvpN5b6XtI4E3ZgP+KDFpF58YgMV5wBRLw+tJZEg5vG7mmmqbmM2dxr1HNERS87SczFqw3HzaWeI2cW2c6edcRgsvTFDxtK/buFEfeoL/ecSdZKZcNk4cXtI1mAqkNRWSfR1JsmuuEwBceJrdDt8Er2fzIVH4QkMWcb9fPT9wJrrRPHyGkA7Tez3ZpH9krqTGh3vmaTlmm7g1KvAytN9n+f/Mf6I8XJEqKu5IyoSw9ywYX8mGoy2jLXtKrxyMBNH1o5CS1cv7v/iEktdj/Syx8E1I3lFcqdKA7mUpu79fR1xeC1/jxEA5g72460aTY72ZBtxSrXWwkqoQ6WBwWDAyq03WVr+5stlEAsFrBjml+eK8K+zxdAZDFg+MhjPT+mHMRHuFirHCxMCoDIKCvk42eC5SRFscX4gvQpbLpfDRiLEmnFhrFhTl1qLd0/ksyrzrd0afHgqH5sfJWXaUHcFzj4/Fqdz6uFmL8WYCOtJq2mz8dDtGpzMrkOIuwKPJQXD0VaCQ7er2Z16nd6Az5OLMDrCHQP8nMzzEQz0d8aB9Bqe8v2vMR1+FxiV9dOv0edb7gqM3UBrLYOWUEFqa3YNCkwkanjeYaIRRhtZOIOWEGWTib/uUSQ+KbGlOGW6o8h45raWkWiS3I3ikbkwmzVEzgCWHKSfc/InqrpAAFSa7bDreikmDVpEryH3CAmyMTubhaeBjF1EER3xlNERw8yuUigmYTeXYBKqu7WDVgKGPHLnc83YQzuTeg1ZYT60laO+X/jQZLXHAFz6mGJ/wio6H0bEKGgUl4w/8C3tparayXOYSUBNHULqMoCiU8CLFZyK/B8Mf8R4CZCFmjWR3gNrRmLrlTJ09GgxL94PA/ycUN7chbeO5pJIZj93vDg10mpD8+P5A7FqTAjauzWID3JhP8fT+nvjo5MF7ATaViLCTBNBM0crgpKOthI0dKow64sUdqAU5++EvatHWM3fBAIBflhGwnMljUpMjvHC6jGhqG7rweNbb7KU9tcO58DHibRCHh1pvSWWWdWObVfLIRaRfXC4pz3mDvbD+79wxeDYfu6sftLi4YG4ZrIKNCrcjdX6OPPcWJzMqYOTXIpx/dzZ4twUDZ0qfJZcyMbFhk41Pk8uxKcLBuHRpCB8fZ4avSKhAE/+Gw5Ldx3VqZbHKq9zqyU+A4GHd1reRygiS7SsvUaRuDmAs3EYGDef8sPcw4BzMLcKc3Q9FecAWaIdfoaEM3u7aZCUfZDi1rR3gaiZFGeLz3DPGTLW8jyOv0DFOQC0V9Da0dO3KIZGzQRCxnHik/7D+xg2GVGcTI4ZYhnZuvkM4jtL3NxEekVyV2DsiyT4aR6nATrmNwQY/AitLAEUe0canUHG/ZXcRcovk0gc4zjEfG/kOsqXzXNsgM6HOSd3K4W46NfdwP4T3JMCffDgwTh79iymT5+OW7duISKCU5aOiYlBQUEBWlpa4ODggNu3b+Ohhx6Cg4MD7O3pDXB1deUF4LuBj04V8Kjo310swZpxoVhoVkxvv1bO87JMLW/FxcImjIv0QK9Wj0/PFOBMbgPCPBR4cWok/F3kCPNQYOMi/uj4YmEjdt2ohIOtBI+PCkGQmx0Sw9xwecME3K5sQ6SXPWtDseN6BdLKWzE02AULhgZggJ8Tdqwcjh3XymErFeOxpGA4yaX46GQ+PjPutx/NqEVObQc+f5g/jREIBPh0wSC8sOc2ChuUGBHiitfuj7F4PwYFOOO0iXJlpJc97GRiKNVabLpUisIGJSZEeVgVhVo/OYK1nRMI6La1KRMA7LhegbeP0VQkp7YDGVVtuPTieMwfGoBDt2vYLm+YhwLDjHYd/bzs8fosrhe6c4vlruGlwiaLY3cNjHiGKUrPc8mRwp0EJO4E/2F8+wgGoeOBJ6+S0JpbOKfUa+7zqFWRWqRUQTs12QeoQE98mkSDTBH7oKVQm88g4NHjQOoWCiwJq7jAs/AnKlqV9cCA+ZScWkN1GpD+I+0pJqyii8fj5+gxYaBAaOqVW3iKim69DhjxJAVqBs1FFhMwNJpQ8EUSYPkvpOqsVtLPmjc/3ML41h+mEEno/p11lrv0NWnWf+YPgj9ivBQKBBbi93qDHiWN/D304gZ63n2pVby98ry6TvySXYdZA33R1t2LtTvTcbGwCW4KGd6cHYOp/c32Xo1YNyEc7vYyXCpsRLS3I1aO5hK+CE97C8vIhcMCUNXaY7Ezf6GQpv/XS1vw3gkuCfzibBEG+jthoommhSmWJwVjuZmF5uXiJjy7i/P1Tiu/iTPrx8DfRY4OlYZHxQSAunY+NdXFjtTbTdGl1mJTSikK6pUYF+nO7mVuu1qOVw5yseBqcTN2rx7Ba1AyyKvtwJIRQXhzdn988Es+unp1WDDUHw8M9uNZBgGWdnR3HeoO7vPd283tVEtsjIJmVuAeAbiv5x+z9yT7yJyfafcv+n7O13bBTiBtC02PI+8DQsbQvuT3k7l999TNwGOnqLisTgXOvUvMmoGLOGVeBqHjOCVzBv4J3M4iQImXt5GpEzmDvhgUnAR2mCR5eUdpn3P0XygJZGj/I58B7Iyiph5RwOQ3+M9ZdIZEjRz8KM7KXajjcvJlrpGr7QFO/Z27BinNdksNeqCrgZLXp25SHJW7Av2m8VXuvfrDApfNNDrUnVQA3Cmp/j/EHzFe3gmeDjZ4YQrftuzxransznppUxeEAkGftmzW7Na8HG2w/8lEbLlSBp0eWDI8kCfKuXJUCE5k1bHN1NkDfRDj44jPzhTy2J63K9uQnNeAKTFeqGzpxg8ppVCqtJg/1B/xQS7wd5HjY6N4J4Mrxc0W++YXChv7FPIsalDiwa84YbdDt2pwZv0YPDk2FC52UpzPb0Q/L3usHM0Jbc2M84GLnRQns+sQ6GrHCtoBgKNcgnnxXDzNre3AhyfzUduuwqyBPlg5KgQtXb28BiUAVk9kw7QojOvngfy6TiSFu90V56XfDJ2VeNz1b+a0Ujm5MFhDv2n0ZQpzLaGmQtJTuvQxkLWPjnVUA/tWkmL67C9J76PyOuWw0/lrRAAo7pqitYzEQmX2lIstPUjxV6+nYYpAQCJ2aVsonxy2koTYqtOAbQ9yrM78E6TK7mDMD7IPAEdMrDcrrgLrMgA/K7m1v9Fn/b6P6frQVECDH1Pbtf5z6csazJulmXspp/WIBIat4r5/+lXLny04Zf0xfyfuSYE+adIkpKSkYMGCBTAYDHj77bexadMmBAQEYMKECVi/fj1WrKCiYOrUqYiIiMAzzzyDV155BTt27IBWq8Ubb7zxK8/y21BhMlEGSBhIpdHB3obfaVSZJVsA2ATsk9MFrI1EXh3ZOJxYN9ri/tdKmrH0h+ts9+5kdh3OvTAOCpkYjrYSjDaZ3Lx+JAdbr9BC5MFbNShp7MLf7ovGsGAXDAvmFyh7U/m7M0czavDuA7GQS/m/xoH+Tjj13BhodPo+J9tvzu4PtZasJPr7OOC9B0nYa/WPqbhkVBY+dLsGbd0aPJIYxPvZsf08cOnFcbhR2opoHwcEG5WNzxc0Iqu6HSNCXTE4gKYI5vY+Tcpe3Kpsw4hQV+xZPQIH02vgppBhyYjAPlkKga5ylDbxf3/O8v/QkuffQV2m5bHr31KHb8yL9C9AYmwVV6hI/S3ei9YKzdDxfFVPO3eaQN3azlms6XqBix9QADadIPeFvpoEQUmk/Hkn1GaQIBNT7GbuocTPOQiYaCVANRYAOxdwE/vKq8CKMyR6wpyLrQvQY7K6YcosAOh9tTaRV3cS1ctcKVNl9OI06MluxNaZAr45zAVA/mD4I8bLT89Yajx8cqoI4yM9eY29CVE0EbYWN1Vs3CzERWNDrUmpxvrdtzEyzI2NvQ2dKuTVdiLOzwmOcgmWDA/EkuGWK0IAsOnRodh2tQKVrd2Y3t8bSeFuUGt1cLGToqWLa8wwSe1tE+cOBrer2tgCfXNKKTZdLoNEJMTa8WFWG5LJZjGsV6fHxcImLEwIgLejLRKCXXjTnjkm9kYVzd1wspPwrNUAYM2ONFY86tDtGrR0afBYUrBFjL9e1oKK5m6MCnfDNxc4er9IKEBiGBV9ixICsXBYAHR6A8QiIc8SlME9dRrsaQMumCRx2h5gy33AX4xca20vkHuIksComb+ueCt34avyMpDKOYcKBje+5YvRVd8k8TXvAcDWOZx3b3UqxYe+kjMGwx6nZuKtnRSDJ79hXS0e4Nw8GLSU0PUgeBTwzG1y0HAJtV4UM8g9TIKbDPKPAasuUJPTXF29y+TvcMB8YnUxcOsHeBub9Qp36+9fX9B0Wx4T3sPr6+/EHzFe/hbUtvdYCMqdzW/A3+6Lhk5vwL7UKqRXtmF4iAvuj/NhhS+LG5U4lVMPP2dbTI3xQrinPd6cbd3G1t9FjnMvjMWlwia42cvYfKyvOK1UazH3X5fRaCzoD6RXY+8Tiez6kikivczVG7h4W9+hwvHMWjjJpZja3ws2EhGOZNTwhN061Vr8kl2HJSOC8PCwAF7xbYqRYW4YGXZntx6VRocl319nxfSyazogl4qxKCEAUd4OPC0n07g8PMSVVcNn8MOlUuy8XgEHWwmenRiBpPB76BQEyxiNCx+RWvuwlTRJBoitmPwWDRmCkoiZxDQsASr0W8toeCLuY4obMYUrxJnbTAPTFNoeoCGX1mHm/3jn0w+bQN7pDPyG8qfeADULGdRnUyOVWYPK2EX5ZM7P/PxM00WCoUz8yj/Of0x1O8X3yBn0Xlz5khoCo5/ncmKBgKyN/11747ZK8nH3G8a5Gl35EvhlA3efims02OqopWuZOXruzWbFniEAACAASURBVMDwnhToQqEQr7/Ol/cPDQ1l/z9jxgzMmDGD931PT098+60Vy5C7BPNuGgALcTeAvCG3Xa1gi3J/F1vWes282Myr60RlSzfrd8vg4K0aHuWwSdmLiwWNmBZrOTXadYO/P7j7ZmWfnVQXhZTdZQLIF1faR1ELgFecb7tajm1Xy2EnE+OZCeEYHeGOHx9L4N2/tr2HLc5Nz8e8QAcAD3sbzBjAvZ73f8nDxrPc8tu7D8Ri/tAAhHkokJzHvW9ioYAt6IcEurD2GXfC6jGhOJfPPy9z5sNdhUgGwEyxWqc2KvIqyVattRzYPIO8GgGiN055i/8z5VdIfVwgBIavsfTaNcWItUQ9zD5AlM9Jr1PB2mBlV6Uhl4JRfTaQc4jsgGIf6ntS9WuoyySKkUBICapHJAVQ00m0sp6m/uaTegaFJ/nWZgY9+f0yBbrUjrqqyW/R9GfAfK4LrO2lc3AJtpya39xEas6abqK2L9xN3VVVO6nItxqVly9+SImtc9B/9h78H+KPGC+ZyYspWrrU+GbpEPzzeB7SK9owNNgZf5lKk6E5g33x7cUSdBgntV4ONuyUPLuGL+zT1atDeXM3+vs64udb1Xh+z21odAbIpSJ8vWQIz+s3u6YdO69XQCoS4ZFEmhI9MTaU93gysQgfzBuAF/dlorFTjTh/J2yYTudl3uQEgIRgSswuFDTitcM57PFnd91ClLcDIjz5iYY1lXbTY98sice/zhejyOgL/PAwfzQr1Xhsy03cqmyDTCzEi1Mj2cl8Q6eKp+wMAHtuVuKxpGC4KfhJllQkhIOtGKPC3fHWnP7YnFIGmUSIp8aF8yY/AoEAYqNavlQstFhPEN1Dhjvyj1oe6zaJ1zvnc6q4Z98Glh2l+KXTAlc3UmPSOw5IetYyyfs1CKxc/wRCoCyFK87Z8zzOFejNxRTfPMzoiiIJTWBMLX30OorLjXnEeAoYTsfv5Ccsd+H74nY1UfLdXk3nEDGFjqeZJcJ1GUDtLWJAxc7jJ8FxD3P/H/AQ0V2z9lNyPvKZO1PS6zKJNWXUF8HYl+jne9r4avYMbPrcEP0/xx8xXgKAUqXBul23kFfXieEhLnjvgQEQWvmduNrJ4GonRTOvoUh/968fzsYW48Bm5/UKFDd24blJEbha0oyl319nd6cnR3vim6Vck16t1eHnWzWoau3B1BgvRPs4QC4VW0y1Hxzihy2Xy1ghTl8nW0yM8sTZ/Aa2OAcoNz6QVsUW6AaDAQYDaXj093XE85Mj8MXZIvRq9bg/zgcPxfujuFGJ2RtTWLbO4CtO2Ls6kfVT570HVo4xKG3qglanR7hJHM6r68A7x/LYxuy6ieEQi4S4XdnGU7oHyNN88fBAbF0+DF+fL0ZFSzemx3pb+MKb4nhmLV4/wl0Llm+5gUsvjrNql3xXIBBZDg6662nt/uQr1NiLnA7sXwUUkqYGqlPps8q40lSnAruWUOPTzh148AdyfDDHfZ9Qc7L8MhXNjPVa0Eg+lV1qb+m20xemvUvxo+Q8xe6pRicFVQdQdZ0ahqbe6xm7+K4dXY1G73IrTk+OJj/naoUxyRwb+9KdbS1NUXmD4i9AiusBxtrn6ldUiBv0FPOW7Kf3KN0sLhccpxhuKpL8X8A9Utn648FaMW6tuA33tMeRp5OwL7UKChsx5sf7s77foR52vM6ng40Y7vaWgcbD2jEH6wHJWS5FnYmVj3Mf1hAA8OJU2mFXafQQCQV4aWokb+pc3tyFn25UQgDg4WEBbOMgOa+eR5tcseUmzr0wFj5mvpN2MjGkYv4EhtkJ0uj0+OFSKa6WNCPWzwlPjAll35derR7fX+Jb1Hx1vgTzhwbgiTGhSCtvxc3yVthKRHhxaj94OlgPegaDARvPFmHXzUo4y6V4fnI/jI5wR2GDJR3NdFp21zH6BeCXPj74uYepQE/5hCvOARJAG/oYNx1qyCNPSqbIzTsGrLnKFZD1ObSH7TuEkjGRmPaxJ77Gf77wyXyLMZGUdoJKLwI/zuaK4sy9wCPGzl7FVRI7EgiAhCe4YGQNzcXAd5OoewrQpHzNNcudUYB/rKuJgj6zx2iNQWB+zDsOWGQmXleXSRQnZR1RWu/7BBhoTES7W4DjL3KBvS6DRD5mfkKJaavJ31x7JV0Eou63PA9zm4w/8auYN8QP6RX86fN9A7zhJJfinw9YXsT9nOU4+vQo7E2tglQsxLx4P3YPcmSYG26UcUWAm0KGCE976PUGvHEkl6VLdvfq8PaxPBx/hoqcooZOnuftgfQqnFk/lo1Jphgf6YkrL7mjvUfDS/7i/J3w9pxYbDxbBJ3egBWjgtnJSEoxv+mnNxB907xAnzvYDxcKG3Essw5ioQCPJAbxCn9HuQQvTeNTWL88V4xbxum9WqvHW8dyMT3WG16ONpBLxbCRCKHSWMbZdRMjcKOsFe09GggEwNrxYaxd0KKEQCxKsM4s4L0Ovd5iL/1Ogn2/G0Fj+v5ebQbfXk2rotj04PdEFbzyBR0vOk3+3QvMxLvUSgCGvgv3oSuBjN1ErwSI5hiYaEnrBCge6fUkpMTYZQaPIf91ia3l/Rkcfpp2IAGy7pnzDe17Jq6liTezCz5kGTU3zaHXk+c7k9xl/ET75NGzLPfVAa44nvkpaZlUp5LuybCV/Pv1f8CScdRZR4mvgx+xsoRCaoAyMRYgHRGZgor6/GOwyq/ovjcTof+fMeWTi6huo+vo3tRqVLR0Y/eqRIv7ScVCvPfgALy4LwNNyl5Eeztgw7Qo6PQG/GQ2sNlxrRzPTYrAdxdLecJmJ3PqUdKoZPWHHt+aivMF1PTbeLYImx8dymt0MghxV+Dw2iTsS6uCXCrG/KH+sJOJrcZUFzuZ8Rwq8MHJfCjVWjw81B9/nxmDp8aH47GkEPTq9Gyc//FKOW+VJq2iDVdKmjF3kC/23KxERhU1zEaEuPJscxno9QY8/VM6jmSQ/sSocDd8uzQeIqEAy364webJX5wtgq1UhDXjwhDkZgexUMDL75nGpbu9jNUh+TWYN0x7tXpcKW62yqi6K/CNB6qu9f390vM0pS48yT+ee4Qr0I+9wMWerkbaLX/aTJ0NoNXGGR9aHh+xlibCWXsBB18aMJnH2fwTZBMsklK8YMR2bRyBWRv59628AWx7gBqjAiEw9V1uD95anLN1pnwtaz/lwgDFM1MrtoTV1MAtu0isntHPU0y0Bk0PcPJvtGrkFgZMeYfYS83FNExj8si8o8ATKWSPduYf3GqWqo0GSEv2E9vTFBI5XSP8hgJCKaA3qz8c7s3fyf9MgW6N9tfcqYadTIzvL5XidmUbhgW7YFliEELdFexkyBQvTY1CYb0ShQ1KONiI8c7cAVYTn0cSg3Ass5ZVv5w90KfPSfFL0yKxfs9toicKyb8RoI7hj8ZO6tIRQejnZY9R4e64/NIEpJW3IsrHgfV9BGj6PfPzS+wEa8f1CpxcNxoeDjaWwUenR0pRE2+PBwAcbCR4ZkI4PjiZD4MBsJeJ8ewk2u9651gefkihguhsfiOKG5XYuLBvyXbGt93ZToq9TySiqrUbTnLpHZXX96VV44OTRK2tbOnByq03kfLSeMitvMfmO1B3FdZEIhgwBXhnvdk3DICygft+7iH+BFrbQ4FhxBoSCTr4JNjEaMo7tLNtDeETgZmfEZVTYgeMeYE6k79s4E+sS8/TXpDYBtgyk98YePIKCRsx0Ou4wjp7P1ecA7Q3mXOIBIdu7SDaJkBBM2Q83d69lAprB19gzlfUtQ2fTBPx9G1UGfR/gBN+0uvIs9za+3rqVS5x1KpIrChmDrEB2sr5XVeAS7zN99mZ55GbGxnhHvN7//9EY6elxU9KURPWTerj4giiVTLxwhRPjg1DR48Wx7Nq4e8sxyv3RbGNwNZu/oXO9Hl/vsWnRrZ2a3Ayuw4LjHTuS0VN0BsMSApzg0RECujWJjMLEwKsMm76W/G5jfGx/BuVioX4ctEQNHSoIBEJ4WxMZq8UN6OkSYnR4e4WLCrzXX2d3oDy5i54OdpAIRPj2YkR+OeJPBgMJBD3nPF96+/riJSXxuN6aTOC3RQs26gvdKm1KKjvRISnPSvwue1aBcz70d1WqK13DU5WpiBC5vdg7cNnPJa5l3847yjtrzO7fidfAa59TZ/1IcuAae9bTok9o6mhmH2QpkjRs6gx6R4BjHuZCmpdL4mlJaymZgFTnAMUN2/vJME5g4ESwfZq2l20c6OJ1S0zKvvVjVSgOwcCa9OAsktEg/cyoRubKiPXpFtOXtK307kmPUtJOLP6E/8YMYkAYlAlPWvl/esDNbcoEWWaFTFzgXmbKFab76wXnqKEu6/Gh99Q68f/hFW0dfeyxTmD66VWmAlGTIjyxJUNE9DS1csOLAwGA+xkYqi1XEy8U77EfLKKGjrZ4hygWLM5pYwt0Fu7elHe0o1obwdIxUKEuCssduKZovlUDuU1IW52WDw8AMWNSrx8MJNt9m25Uo5oHwfMH0rOF7bgcjO9uYKn8VzsZGIcfHIkrpe1QCISYHCAM0vbN0VyXgNbnAPAxcIm/HyrGjE+jrwhFgCcz2/EmnFh8HSwwSszovDPE3lQafQYHOD0H4m+5ddb2mGa2x/fVZhq91iDdxwVxY7+JMLGwHQ9qKmI/zMtpVzcaSmh6bZnf8C/j8+yWArM+IC+rKE6FfjpYS7XKjkHPHW9b5Zi8usca8mgp+J30GLjzvwjFPOajflb8BhiIwlFwPLj1MiV2FoOdWwcgGVHiLEqs+cYlm0VtProaaKvde4dypMBoLMG2Pkw8Mwt0uMwzSN1asrP4xZarvcojXn9uL8C2+dx61NjX+J835fsp4ar6ZrCw5auSXcD/zMFujXIpCL8dX8m9httLE7m1KO6rQevzuR+6Tq9ARqdHjYSEQJc5Tj57GhUtvTAw0HW51TCxU6KE+tG42ZZCxzlEnY/51ROPfalVsFVIcXqMaHwd5Fj9iBfDA12QUZlGwYFOMPL0QaVLd2Y++Vl1mrsYHo1Tj43Br5OtnCxk1oVODp8u4YtzgGgrVuDIxm1WJ4UzKMKMehnskfUq9VDLBRAKBRgzbgwTOvvheLGLiSEuLC7k4duV/N+/nhmLdRaHWRiEaRiIZYlBuOr8xzFfdUYPg3Vz5mfwN4sa8GRjFr4OtliwTB/2NtIkGJGr1dr9bhZ1oLaNssdufImpcWxuwYT2zIe7H2AqW/T/+Pm86mdLiH8pMZaR40Jyuf+CV7iev5d2q1kLlq9XbRL7d4PUHiQ2u+QR/iPJbTy0RWKLRsDOjVN/ZPWEU3zxAbqtvZ/gAp/qzRNN/p68hopcUrtgMCRdH4nNnA7+h3VwIEngHWZlDjf/zkw/m9UKDMiH/knSOSjs5YK+Qc3cYJJAAVaU6ja6UtiA3jGkoWH6QWKEUCJmUO0dqaDrPAk2mfVdcvXo7MsNv/EnZFeaek3e6O8DQfTq1mq4DcXirH9WgXspFRgmsYllUaHXp0eDjYSSMVC/H1mNP4+kz/NkIqFmBHrjUO3OatL0z1Ba0rETnIpVBod5n9zld0vj/Syx94nEtlk9mpJMz49XYgOlQYPDwvA4j722WfEeuNmWQt2Xq+EWCTA6jGhiA/qe+XGw4T589qhbGy+XMa+js3LhrI74QAwKdoLZ00ao24KGc9NY9WYUEyO8UJRgxLDglzgaKKpoZCJMT6Sey+rWrtxNKMWLnZSzIzzYa87Fwsb8eS2NHSqtbCXifHl4sEYFe6OE5l8qzoAFur3dxV5xyyP6Y0JkXccX31dJKNCGaDVHNPC0c6d0/coTuaLl934juKHKW2cgaMfkPiU5fExf6Gps7qTU0duK7O8HxODDqym6TZAKuvLjlIRLhDxG4KmsVdiw991LL9MDhzNxTQBm/O1dacPVjQukkSPSs7R6/AZZHlfa8j52bgaMBAYuJAS3cufc8U5QM3XsRuo4BfbcPZuAOBh/CxGmOmAMLjwPjB7o/Xv/QkLKNWWrjSmyKpuR127CiPD3FjmoUQk5LEJBQIB1k+OwCsHs2AwkM7E+snUEF2eFITzBQ3sYGJilCc7KRZbodEz6y67b1TilZ+z0KvVw9NBhi3Lh1kVnRMIBPh2aTzSKlqhVGkxItQVEpEQ5wsaLWLHrco2zB9q2fBclBCIvalVbN4a7e3A7pALhQLeznd1Ww92XquARq/H/Hh/hLgrUNVqmedVtfZgan9vC8ZRhBe33rNsZDAejPdHW3evRZ5pDbXtPThyuxYOtmLMjPOBXCpGVpXl9W7j2WJM6UPM9HfD2h6zWEZsm0GLaQVQIKBVm32P0XRX4UnUcgb9pnHxCqAhiUhM8Xj3Em6AM+ZFKjgNBhJpyz9BDcyR66zHJgZ5R/lxT6cmYUxmKq7poak204hU8leA0aukXFYqp+d54jLlkxI57dObNmlMqfWN+TTokcipMevgzSnVA8DhdSQGCgMxphbtpUK+2MwGuL2C4rDVXNyXHjd4NF/7iRHGDBpJeW15Cq1BmTYOFJ6w0BBI/gewaA/uNv5nCnRr+YlSreUlhwCJYzAF+t7UKrx9LBdt3b2YFuuND+fFsYU6A7VWBwEEFirmIqEACSYB6WxeA1ZuvcnePpPbgHMvjIWNRARfJ1veNPxoZi0b5ADa2TyWUctTuQSoecDYbpjbowFc93V+vD+ulTTjaGYtJCIhnhgTigF+TujV6vHygUwcSK+Gg60EL07th/lDAxDiruBZtwGkRMp4TwKUcJquCLw0LRIJwS7IrG5HYqgrm+jq9QZ0qrW8ZPtsfgOWb77BBv5jWbU48ORIxPg4sJ6fgNGxwdsBrxwwU4wEcLHoHlLwuq10vu3c6QPLBKPoWcBDP9I0xsEXGPk0N5UGqADO+In78IdPASKNquZas6mwVk2BUCACqm4C2x+kyY3QuA85eInl+Yx4igItM/2OvI+Svdrblvd19CNKOuPfDhAd3C0cGP4kBbvaW3Q8YASnvi6WcruSDMzVOzuq6OLBBHqFiY2UpofopCrjxa/0Avki3/8Zd5+YOUS5ZBCQyIkyicTUrUx+gzqo0bPofAF6vlUX6XUYdHRBU3jQasGf+N2Q9mHLdSavAbMH+eJYZi3rzgAAq7el4twLY+HnLMfmlFJ8cLIAXb1aTOvvhY8eGthnM/O9Bwcg3EOBjOp2jAhxxdIR3IV4Xrw/dt+sREE9FR0JwS6YEOWBIxk1PPG3vLpOHEyvxuLhgWjsVGPZputsMvfKwSy428swJcYLde0qfHuxBA2daswe6IMJUZ74x6z+2DA9CkKBZQw/m9+AL88WQa3VY1liEOYOpklxk1KNH6+Ws/fr1erx5bliXoH+8DB/dPfS9cXLwQbPTY6weA+C3ex+dUKeV9eBB768zO6Mbr9Wgf1PJEIoFODVQ9mszVKnWotXD2Ujef1YBLrZ4XJJy50e9u7CPCaYY+FumnB3VBOlkRHInPwmsGMBTV1EMmDqO1wMtfaY9dkUA3IPE01eKKIpcJhJgaxqJ+0P9whqmto68+mVEVOJBslMTgQiOqemIn6yq+4ALn8GPPAd6XJc3cjdP+k57n6mbCSdFti7nLOIKzpNyuuzvyQVYGZVyc6DPxmX2fMdLwBaU7rxHU3Rhj/JF5q79DFZ2jGoTqW1H62VRqS2h2LlfZ8QO0nVTjF2jNFWTdSHGBzDnPoT/xZ0d2D0bdifiZ3XqQnkbi/DnlUjENTH535RQiASgl2RUdWG+EAXNtdMDHXD8WdG4ZdsEombbqJnFORmhxkDvHHUOH2WiYVYOSoEPb06vH4kh2WP1neo8f6JfHy/jAYJxY1KlDd3ISHYlc0fGTE5BkON1m6mGk6Mhoc5+nnZ48Qzo3E4owZOcglmD/S1au/YrFTj/s8vsTv4269W4PgzozAhyhPvHM9jWVMioQBTYrzgaCvBO3Nj8erP2ehQaREf6Ix1E/lMLYVMzGMbVLZ0453juSioJ12Q9ZMjIBOLUNKoxCyTPfktl8tx6KmRsPbr61DdwzVKa8K14TOAuV/y123CJwLP5dI6n2s4Xwhuxoe0dliWQlo/E1+j4xfe47MrUz6lOHnzB2IlAbRTXXmDptcAxb+rG4nBNGQZeZo7W7HMcwkBNCrg5zXUALRxop32wUsoBzMdboVOIMFKBtbySXM0FQHfjOMm12lbiCFlY2S7VVzjLNQAoPIava6kdVTk12Vw37NxIrapcxBdIxhRzZBxxC4CKIe//Bk1BRgWKAM7V3IRYaDXUzw9YoXVZCrYeRfxP1OgW0NuTTtcFVKerRqzP17focJL+zLY3ZajGbWI8rLHU+O5Tso7x3Kx+XIZhAIBVo4KxnOT+6Z/HrzFn0DXdahwtaQZY/tZ+uJa3wfijl0qbMLLBzNR2dKNiVGeeH9eHGYN9MWWy2VsMhvpZY/74iiIS8VCfLFwMN7o6oVELGQD2fZr5dhjVA1u6erFhv2ZSAx1s6BrAsDL06Pw+I+pUKq17ETMnKY0LpK85BlcK2nG+j23UdXagzh/J2xcOAh+znLsuFbB68qmV7Qhq7odS0YE4nZVO45m1MBOJsYLU/oh0NUOHSrL7rSVjYW7B2v0aaGYK84ZRN/P/wCbQmIDPHKYCmaBiJ9gJTxOhSqDYSu4JO/Uq5xoj15DVjsDHuImSwz84olulHeMCnDTyXLGLk78I2wSJaDllyztx6rTaDq+MplomkIx7W9aoZ+xCBsPpG3lbnsP7LsL21rOFecMTAMoQNQhmcJoZxHNJY4M3MJpX9Ma7FwtVwN6/3v2Of8/Q9iHqliYsXFnLiap1RtwraQF2kAD/nEkh2u+ZdYhzq+MZdRcKmzClitlkIqEWDEqGIMCnLF2gnUHBEdbCY4+PQopRU2QioUYEeIKgUCAjh5Li5r2HooRl4ubeJMWgJqjEyI9sOCbKygzunkcvl2Db5fGY1K0p9XmQVlTF1ZuucnG/+d234aPky2Gh7hCo9NbiI6aqyMLBAKsGBWCFaO4pmqHSoMvzxYju6Ydo8LdsHxkMKsh0qnS4PXDObhY2IQob3u8dn8MAl3t8OOVcrY4B2h6da20BSNCXXm2dgDY20+MDsXO6/xd1nsKaw1NU4hlxDgyR2Ai2frU3iJvWTuTxD90HHDKTOoudDxQZRRGYo6XXyHbSrcw4w7kXKO9mYAU2Bk1ZAaOfhSXUz6leDhsJSW31hqbvcYifurblCg35NI5eETR/w+spnP3Hw7M/Zqes9OMvVBlbMpPf4+Sv/Yqmtrcyf+8PhvYfB9nsZZzCFh7k7yKAeDGD/z7p28jkaahK0gIj0n+A0YQgwEgXY+YORSP+1KlN8Wfa0G/CYI+GppFDUq2OAeAxk41vr5QgnfmcusQ10qayU3H1xETozwQ5qHoQ5jSHmEe1lcSPlswCLMH+qKypRuToj3h7yJHbXsPlGp+rGRixEenCvDZGaIbO8sl2LFyOKK8LSfrjM3ahyfzoVRpsSghALMG9k3PDnCVY40ZxdxgMECrN7CixSey63gCeUq1FgfTq7F2Qjh2rByOby+UQKvX45HEIPT3pcJsziA/TOvvjQ6VhhVuU2l0OJlTD51ej8nRXrwh1cqtN5FXR3pRRQ1KGAwGvDwjGjuuVfD25HNqO1iHEXPcab3gnkCntq6FIZXzqdwMZAr+RJ2Bhn9dgE5DBXvGLv7xisukYC5TAN9P4tZssvYCK05T3pl3lIp5CIipEzaB9JeyjOtJPS20+x46jpqOclegyJjLjTCymtoqqInoN5QvCpd9gEQybZ2BUetpXen2Tr4rR2ct5biMLpE549L02IRXKecsu0j75TM/5d7Pxfuo6WvQ86f1tk7AhL9bPqY5Ck4SE7SjGhBa0TSyVjPcBfxPF+ie9jZ4ZUY01u++jV6dHjYSITZMJ1XX3NoOC2G5zGqu2DidU4+vTWxvPksuQkKIK0aGuaFDpcGem1VoVqoxa6Av+nnZw8uKMJqXIx27WdaCnNoOJIa6IcxDgZkDfLDjWgUrMjQ4wIlVTFdpdHhqZxrauunifTKnHj6nCvDa/TE4vDYJZ/MaIRAA4/p5WEyEnM0K/wwzWo/eQFQsfxc5mpRqnMiqg6OtBJNjPJEY5oYrG8Yjs7odkV4OcLGTQqc30K59fSfGRnrwuq86vQHP7rrFqs7frmzD64dz8M3SeMillgmxXCqCTCzC5w8Pwj/nxkIiErLn7+9ki2IzmzwrD3GPIaRpSdY+8iwPnUA+vAw0KiqKZfa078gUuUyCZIpR6ykhLTd2PpluHkC7M6ZQtXNF57WvaZ8xKIl2xJ0CgOGr+fcXS2nqXJdF58AEdp9BRBky3bkJNAp+CEX812IwUCKp8LS07pjyNgAB0Ym8YmnqBdC058KHlFSOWEPB2jWMxIo6TKyjQsaava3GKdjIZ/jHa27RhcY/wXLvtPwyMQLCJnB7QQAJqKRtw5/4/bC2f+doK8HypCAA1ne1Y3wdkFfXaUGJZKxusqrb8cim62xxeyavHmfWj+Wxh5Lz6nGlmIQo74v1hkQktGhiTov1wkenCtii3E4qwv1xlDSGW0lgwzwUuFneyhbnDPanVbFiRZ0qDVQaPSv6ebGw0SL+n81rwPAQV3g72mJKjCd+yeZ0KJYanS5UGh1Sy1vh62RrMSV7Zmc6S3u/WNiE1m4NqznyxpEctlla16FCw/Y0HH16lMVrMcV9sd7sehZzGwD8XX+d5nlX4WPd6gkdNb++aylTUDwzh1csCcld+pgmF4lPkXp68lvgVY96De1wu4UBZ9/kvMdhoPsOWWa5Z+0Xb2kj5B1HNjvMioxASHvpDELH88WLmOIcIEvJQ08DSw5STDZNIhlRJYCatKaN2rYKosNXXCMbyvs/Jypn1n6uOAeA3k4Sc2POx7y4l9hSHA0dR4l1zkGKu4MWmd3PzJO+u4VbPTCHwz2i9v5/Cvs+irm2bsspbKtJcbr9WjledDz3GwAAIABJREFUNmEIrhodwuagaq0Oh2/Xoqq1G1NivHgFdGF9Jy4WNiHSyx6JYW4QCQUWwmvejrYYHOCENBOxzxkDvNGkVOPLs9z+cmu3Bp+dKcS/FpPLTE5NB0qalEgMdYOLnRT3x/mw8dUc5wsa8caRHNS1q3D/QB+8NjOGl3fuS63CO8dz0datwexBvnh7TqzVwldhQ8eGBDpjyBLrbjc2EhHbTO3p1WH2xhRWtNnfpQCH1iTB2U6KmrYetjhnkJzXgJdn9C0YJxYCZn1d+DrfQTjyXiAoifLI3MMUx6Jn8dcBO2qBhhyKX8xE2RqGPQ4cNWH5DJhP97f34VsIi22pQM07yre/1WuBjD3UmFz4E7FphBJOlb3mFv/5DDrKNx39LNcxM3ZTrDToaAA09xtilxaeAvYs4+5XnEzWlNYalzKTZlXYBFo/YuM8uLUnhQftq6vaAamCz2gF7mx3yUCrpp11TQ+xUm2dqFG7fwU3bNL/91Ym/6cLdDd7GUb188DwEFfk1HYgzs+RVcwd5O8MuVTEo5onhnIfFtNi3fTY8BBXzP/6KpuUfnexFLtXj8Bjo4JxKqceJU1UbC0eHoBILwdeJ1MoADYuHIxpsd7Y/0QirpY2QygQYFiQC4TGDm1ZcxdbnDNINxbyMrEIU/vzbTUAukg0KdUIdVfwpt4jQlx5lHKpSIghgc4ob+7C7I0paDU+z5BAZ+xZNQL2NhLee7BhfwZ236Sk8vOzRfhk/kBW9bK1u5dnCQeQRyUArBodiuTcBpaeOXewL49Sb07XF4stu9N3spf73TBYTujQbewUMvYLKZ9SQjV4Ke3efD+J/CgBSuQW7eMXlnVZZNMTPJoCSeR0+jJH7DzaSWcQNpEm1AdWc5Y72fspuZvUx648YBmMbJ0pKT35N+pKDpjP7YKaoqmIhEGaCojWP+crPoVUZs+nqAPUHT26nrt9+jXaHw+fCDy8k/bWW4rJu3KMiTq+upOoraZNAL2e9qfyjhhfxwDaBWUE5nYvpf1LAFB4AY+dpKT24od8VsKf+F3Iq7PUeLCTiljv8ofi/ZFe0YYD6dWwlYjw9IQwRHo5wMPexmJfkBErOpFVx5s8qzR6nMmtx9IRQQCA7y6W4M2jnK1gekUrTw+EgYe9DQ6uGYltV8uh0xuwKIFzrIj2ccC6ieH48lwxerV6TIzyxOLhgRbTZgBsMf7ZmULWMmhStCc+f3iQ1UmV6VTrs4cH4UBaNYoblZgY5YmEEFeUNnVhwTdXWEbWU+PC8PwUYlV1qDS8nXQAOHSrhi3QL5lNcbJrOtDS1YulI4JwML2anaIPCnBCglFB/q05sfB2skFqeSuGBDrjqXHERLinAprWYGqLY4pPBgDztwH9jHvO1ak0NXHwBQYt4Sde3S20n+gSwu1hW1Mpv5PtTpfZJEzbQ81NmT0VokxTMWaudUuyJfuB1C3UnIyZ07f7hU7LFecMqlPpMedvo1jYmE90zol3iNE/r+FWoErP0+1lR7hJuSkUJsfG/RXY8yg3KR/zIkdV9x3MWVsyqLpJiX3wGG6XszoN2DqLn+j+if8YHVYKcQAYFOCMcA8FKxgMAPPiuSnitxf4qwSbL5fhOSMde9WPqazA7xfJRdiyfBhGhrnhRFYtntyexgpBrhoTgg3TzOwCjfhmaTw+P1PIUr0fSwpGSVOXRfOx2bi+aGqXaycV4ccVCRa0d/Y1qzR4clsqG5t2XKuAr5MtO0GvaevBX/ZlsDF/b2oVIr3ssXh4IGJ9HdkcOsTdjl0fMseFgkYcyyStoqWJQeyq5PGsWp6jUmVLD/anV+OxpGC4KqRwlkvY/BXgGrcLEwKw62YlO0WP8XHAqHA3i+IcAHJr7uVnQwiLPWanQGDTVBrCAMDZt4CVZ+kzm/YjTXD1WrJDW7iLa/511NJwyCWEWElDH6OfKTpDInEDjOyl8S8D1TeB7mZidU58lWKjwpLFyztmKkwHUA6bc5C7LbalBqM1nHqVi1N6LeWG/R+g64ApelooBg5+hGJwm3GFzG8oXydD7kIx8tLHlD8OeZQ/XAIsmxc9bVTUm8b8tgqjM4eA6PmOfuR28f0kjk2V/Bbw+Fm6NpkzQc0hsWS83A38Txfoe1Kr8XcfR7jbyzDGnm9J4SiX4Nul8fjn8Tw0dqoxd7AvlpiIDSWGuuLTM3wrl8RQV1wrbWaLc4AU07dfLcf78+Jw8tnRSKtog4udFGEeCqg0OnxzgRNW0xuAz5OLMC3WG0KhgFcMMwhxU8BNIeXtgydY8fpl8MOlUvzzRB56tXpEetljy/JhrDDJvHg/VLZ2Y9cNsjV7YUo/eDjY4M0jObzgllreipTiJp5tR2tXL/amcpNRgwH4/lIpW6C7KWSI9LLndTKTjDua0T4OOP+XcTiX38DSRvtCr1aPpk7Li58B99LY10rwFImtqPl+RQX6zR+44hygbmDJWer2AcD59yjYAhTMFu0BgvuYjo15iSweis/Q9DvpWaIoZZoJUNzawRXoWjUV3jk/077N1LfJvs0cYRP5xbY1nHiJinOAxOR+fgp4NtuyG2mK8iuWxyouU4HuPQB41MwnWaMCDj5BQV5qD0z4G2chVJzMFecAUeLTttIErTqVK84BEpi69hVN8a99c+fX9Sd+E+rbLbvEporrEpEQH8yLw+uzYiAWcmwXFzspvn9kKD44mY/Wrl48NNQfDwyh5MvaRMJ0er4ppYz3ve3XKrBhWhS0ej1eP5yDM3kNCHNX4B+zYhDhaY+/9WGhs25iBJYnBUOl0bF0yDAPBZaOCMRWozOGr5MtHh8dgtzaDnx0qoD92VM59dh2tZzo6UnB2HKlDFq9AffH+fB8dGViERYM4wslfZ5cyFuX+vJcERYPD4SXow1sJSKLpNHHiZtmRvs48hqavk62cLKVwMVOil+eHY1jmbVwsZPhvgHebLPWViqyUGMGcI8V4ayg5Lz143oNTbX7TaXieNsDXLKWtR9YcYr+X51Gqri9xmvFiKfI8sca+j9AvsBZ+2jKPWgJKa4DRMH85a/cfUPHU7F7cxMltwzKLtHOtjlk9tbF5swhEvOn7QAlxgBN4leY7SMWGnfRlfVA3ALa2RSKLONmudFqKO5h4PZPlEwDpF9iursZPQtYm0oMLO84vnq8Oc6+zTV8hRJqmIZPIhG4OxXnjfl9f+9PWEDYB8VdJBRg5+PDsSmlFPUdaswa6MPLo4Rm62RCgQACCFDU0Mlz39HqDdiUUoqRYW7YeLaY59Kw6VIZ1o4Ph0Imxtn8BuxPq4arnRQrR4fA18kW/5jFb9aHeSgwKMCJZ6P54BA/tHT14uvzXMOgq1eHz84UYvOjVHzVtvegoF6JQQFOcLCRILu6g7d+AwDXS1uwZhz9P6u63WIV6HZVO1ZIRNj/ZCLO5zdCq9djbD8Pq2tGJ7JqsXpbGnv7dG49fn6K2Dbma0x0jM5FJhbh3QfIxq61W4N+nvb4q5GVEOKuwMlnR+NoRi0cbCS4L86bZ1Vs7fHuDax0BPIPc8U5QIV06mZqyJ36G7dX3ttJFpUrTpNWxdbZnBbRsMeB6e9bz/W844B1WUDVDWpqOhqvZ8FjgX4zONFjjxhLUWJTDHmU2FG3d9KEf8Kr3JqjXs8vhHvM1p96jH9zTvxrJ3vMzo1WlgpOEDMobJLlaql3HDBvM/9Y6QWg8jqxrBhGVns1TemrrlNTeNYXdE3oqAW+HsOxBm58R89ZcZm/6tRZQ42RpHXEPjBnt5rCfH30LuF/ukD/IaUUAwOcWPqOUq3F6Zx62EiEGB/piZFhbji81gr9DkBCiCvenN0f314sgVAgwBNjSXgttdxyH48JPmKRkOefq9MboDWbdqi1dw4KUrEQXy8ZglcPZaO8qRuTY7ywbiJNTrrUWtS2qxDiZgehUICGThXePpbLdkvz6jrxeXIh3pxNF3RSDe3HKoUysOYZb36eQoEAQoGAZ61hLgjy1WI6z//H3neHR1lg3Z/pJWXSew/phCQQCDV0BAEBKaLgioBgV2yrrq6uYlfWjg1RaSLFRu+9h4QkkBDSC+k9M5k+vz/uvG1morv7iZ/fbz3Pkwcyfd7J3PeWc88pbuhCdpw/npvGdXl93OSCrmludTs+PlKGXqMFC4dGYLJdPXPlzitodWAMAOTLfsMgkgI2hy+cype+hBbe58NMfl111/T2QGTopukuA3MvJUzRo2gSs/tJKrbVvpS4pc4h2jqfum610gRcy5vA8alPx9/hBIh6Gkh4acVlZ3q6I3RtlOT5J3IqlY6JWXc9BVU3X7p90c80+Uqcxu3Fu7Lk+SWbnnOfEQsAIIGoXU/SycQn2tkOiHlPABfc+WAu+yUf4z/xb8OVuI/VBty55iwUUgnuGxODQZE+UMudTyEj+vmx6r18zMoIxY786zhZ2gqA6JZjefR1hUyYKMklYohFwJt7rrL+wM3dBiz75gIOPzGGZQN16U1Yd7oKte06TOkfjOx4f3gqZawDBYOXZvTHwqGRaOzSY0i0DxRSiZNIKABcs+t4PDctGQ+Ni4PZamXt22radDhR2oK4AHcnxffmbqH4o9VGgnJBGiVkEjFemJ6Cp7blw2i2wkstw9O8qdcL05PR1K1Hfm0nQr1UWDUvjU36w7zVWJYtdMVwhZo2HR7fcgnnKn5HgTgA+KVmaa89Nl5YIxRGqj1HyWhIBsUvI4+SemY17Y57BFGzoewQp4ruGwvM+ZIE5kQS4T71sAcojpbspT3xofdxj8dH7jq6v0ROz11mnzSN/RsnamS10K5lyV5y0xjzrJD2feunxKiqzaFJ1nQHVhEDXRuweSGXQJ/+kBLRrOUUIxn/X4CoqwDF16UHKJGWyIGQdOfH9Ynm7NgAKvYrT9DxZFTlDd3ACV4jwmqiZnHcxF+fCHn8SXH/d/BLpBU/d4XrRhqA+8bE4smtnC7L0lHRkEvFToU7wMVkx9zHYrPBarPhaEkz7l57nr183+UGHH5yDBRS5+J37aLBWHOiApWtOkxOCcLUAcGobdc55X7MGtHGs9V4/sdCWKw2uCuk+HLRYCQHezqxpTIivHj/92btNBkMjaGYKZOIBa4fJrv1r5tCisH2uOroCX+pthOXr3ciJUSDm1OD8O6BEjTZY65GJRM4gExKCcLoBH+09BgFTWCAqP98bZC+IJP8znuUBmcVe1hNRLd2zH0Ym9/j7whtcs9/QSuUrlg4AFHIHSfOYjFw+0ZqlJp0pF0hlhDr6NQHtF899AEurojFNFQZ/zzvtXcTA6hoB1Hhp75DOd3Av3C5KUANVYDiX8kee0NCRL8z66ByNdCft/bZUEgN1oZCOgdMf1+oV3LiXWpYMJi0ks4f+57jmqhddcD25aR5UrhVSOnXtVA+6uh9DpDwpkRGx2fPM3QeEsuAbqGmmEvW7W+A/+oCHaCJyS1pIWjuNmDmRydZL8u0MA223DtcsE/T2mNATlU7koI9Ee6jxsKhkU4WPoMivTEqzo8VntCoZFg0Isrlc7sppJibGS4QEbl7BHfSzalqwz/3X0Or1oi5g8KweGS0/Tl8sOMh4QT2x7w6PLu9AFqjBdF+bvhy0WB06IxOAbeq1UUQcMDtQyKw+XwNeu0dxPhAd4yMEybcGrUMC4dGslZDEjHZFPER5eeGrxc7U19sNpuAat/UrceCL86y6wQny1qw6Z6hGBrjK/DFdHiQX30f/zFsLixTetsoATyxin4XSSgQAjTxOP8F10XzCKapB0DTYkd1XSbYnv+Cpu+A3bJsOQVHTajw9mIxFe8/PkhJrkQhpE46TrC0TURpDEmn43Rpk33fPZMCplhCndf1szlbngkv0rQ+biIl0wxCB1Ew7KwldU2t3UojZCDRyyUyooJOWgkce5sS22H3c6J11/OIARCWye3lOwrFwUZ7/T7RRGdSargEUiwF+s+h/0dnAz6xRJdnPoOMhfT/MU/TGsCf6ka/CXqNzt8Bg9nKxrUTpc04/MQYBGso+Smo7USL1oBhMb59KrYrZRJsWDoUxQ1dkEnErE0Qg4fHxWHFd3nsV/uBsf0glYid7BcrW3Wobe9lae13fXmOnQZtOleDjxcMFKgc8xEf6IF4nu0kvV5hkskXuuTbnx0tacbSr8+zFPJ7R8fi6Slc4j0rI1QgOJQQ6CHY1Z+ZEYrseH+UNvUgNVTD2i0BJMb004Mj0akzwUMp7XMiBwANnXq8trsIRfVdGBXnjycmJUAll+CZ7QX/C8U5hPRrRzCTGKmLBprMvm/oWCzaLIChB/AA7VIyMVIsI/ZR7Ni+d9vT5tMPHxKHRqVYStP3Qy9xVm6154k5dLfdMu7YW+SrC5BScH0+sJwXZ31iSGzu11CXI0ygARIxylpOE50f7qPHDxsMzOQ1EkQiIW3UZiMmUel+mnANf5DbrT//hXDFaNQTlDxbTMJddoDTIBl4F50T+oKjKOmf+EVY+hgY3PH5Gbx7W7rAopGPuZnhSAjyIJG4EA2bZ8X4u+Pm1CDsKqDmtNyuzA4Ai0dG4yleUT8vMwyeShm+v1greOzrnXqcLiMh4po2HbZdrIVcKsbcQeHw91A4DWbCvNWC3BWgXNBgtuC13UXsNLzHYMabe4qx9b7heH9+Bv7x8xU0desxfUCIIAf091Bg9YKBeGNPMdq0RswZFI7bXdiztWuNmPPJKZQ10/pndrw/vlo02KnBKhKBvcxLLceOh0biuws1MFttmDMoDCEOhbhCKhEU59WtOtS26zAw0ps9R3XojPgnj0HFh8l8Y4quPuERBPjFcwxGuQd9T5WetB7IZxYyoptGh3zeZhWKxJUeAI68QXne4CUkJAkQnbvoJ5rSJ02neMpfjWkuAdbP4eJH+VFg+bG+97iPvM6xG9srgS2LgceLiN3on8BNtxmVdJU3sOwI7cQzauuuYLMBW+4CWu2aCcU76Lwx+3PuNicd2FAn3qUCnb9vD1Du2t3gepgjU1HOytcQUXhyOh4hGcDiPfT/bcuBgm8dHuDGrNz+1xfoMXYhn+8u1LDFOUDdukPFjewk91hJM5atuwC9yQqxCFg5MxV3ZLmgaQD46u4hOFTchNYeAyYmB8LXXYGqVi3+ub8Ete29uDk1GHePiIJIJMLKmf2RFe2DovoujIzzY+lPnToT7vryPKvC+dKOK/B1l7MUcj70Jgue+6GQpRtVtGjx5p5ivH97BkK9VIL3NSmFkqnGLj125NfDXSHBtAEhgr3vhCAP7H5kFH7Muw6NSopbB4VBJhGjtl2HnKp2pId7IdLXDS9MT8aYBH9ca+xBdrw/661uNFvRoTM6nZSsVhte2VWEDWer4K6Q4vFJCbh9SASOXm0W7PrbbLSvOjTGF6FeKrRpnekjvyQ0/j+HiyLPpKWdnX4TSGU3ZgxZ+QBE416yj3ZaFB7A4Hu4/Up3fyF9COCSVkfPbquZOoqaUAq8lSdoNyYwmeib0dmUKIYPEU7Qg9NIqIiB3J0mTQBZlDET/Nz1VAhPfYeoj3zP3CN2FeBJK6noLj1IwXjSSrr+wlquOAeA6xcp+DOF+PCHhIrJFjPtsl/bR7+HDQb+8iOJusWMFVL2pSpqTAD0vhbv4yw/MhdzEySJjILkuc+p6zlgPrcnmjafFOWPvgVc3ur46f2JfxMWm4OCtgP0JisOFzfjjqwIPLY5jxUrC/VSYcu9wxBi/96+d6AExQ3dyI73x/LsGEglYpc+vAAVsCkhnjhTTorGGfbdx/6hGsEOp5+7nBXYLG7oElA1AWDTuWq2QO/QGbH6aBnKmrSYkBTgREv391Bg7aIhePdACbr0ZtwxJNyljgcAfHjommC/e82Jctw/NpZNGm8dSHFyZ349wrxVWDY6xsnpwsdNzrKobDYbTpe1osdgRna8P5QyiaAhkFfTgYLaDmTF+AqaCvdtyGHfc0ljD4xmK16e2d8le+t3gboPwaJbvwAGzKX/D3+IFMaZSXnKrZS4AZSEVh7n7hc5kkTfeprsfrd2WE20exg7lrvs3OdAwVaKmWOe5Szc+Mh+nOzPGKXd4Q/RlKbIocCuOgloW6khecXBp7g+jxI3V9RMgF7rD/fTalNQKumTBKXSj1gmLJRD7ImwbyydN2w24Qmtt50m7768hveJVZzGRtHPVPgvtMe5kw7T+zMfU8NS7UPHuZAXDwcvoX/TbqMkuegnTleFD5WX82V/ok909BpcXn6qrBUv7biCD+8YiOsdvXjCznAZEKbBm3PS0C/AHQPCvDAgzPl4f3D7QMxIb0Rtey8mJgWylmvzMsMR6aPGsWvNSAjyxFR7rGM0Nfjw91Cgtl2Hqe8fR5d973rd6SrseTRbYH3L4JOFg7DuTBUqmrWYmByICcmB6Ow1OanBN/fQ+52UEoRJKUFOQxcG45MCMT7pl10DNp2vZotzgHLtY9eacf/YWBwtaWan+AuzIgUOQwGeSoGrktZgxnsHr+FcRRsGRnhjxcQ4VjPl3QMleO/gNdhs5NS0adlQxPq746FNuX2quMv6aDTfMEjkxJy59C2tn6TOpZVFgMTVTn1A+V/sWCDT/j0evIQcehgwTESAFNo33c4NjnY+TuKR8TcB62Zx9zu0kuJQAE/HoGSPMGbZLCRUyRTo+k6KW0ycYNwqGBg6qdEQkmFvDCxx/Z756zndjUTlv55Hue6EF6nZwBTnDKrP4F9CzBiglbeG7BtH+XTqXFpPZa7zT6Q4qXCnnf/c9fS8afOFLCUGulbny/6coP/2SArywJJR9AH0Gp2p5b28HZS39l5lpyxWG/DGnmLMywxj91f4AcpRUdNiteHONedQ3UbdrgtV7ZCIRbhreBQkYhFmZoQK9hsB4Fxlm1NQPFjU5LJAb+kxCKwjAKC8WQuZRIx1S4bgnweuoa5dh+lpIViYFYHqVh2mf3iCDXxrT1bipwdHCtgCUX5ueGQCF/x25F/HI9/mwWK1QSwCXr91AOYNDseYhACByvKugnr87fsCtOtMSAv3wmd3DmJ33rfn1mHNiQoAgN5kxLPfF2BwlA8ifZ09QSPtJ6PnpyXjtk9PO5UKLj6uGwuGnhk1QqjMyyAkgxM3csScNTQFaioir0XGmi1iGO1SMhDLaNLcUgqsncIVxEMfoL1yTZjQpoLBWLsIW+kBmmZNfYebruR8LbztxXXAlLcoAeTDrCdBJY8g19YdrnZsfmnvpmQPV5wDNKG6tImaAOl3cPs9al+a9vAbDgGJlOC6gnsAiZ24QkAiEJzxZ4H+W+BfYKhE+qpRWNcpUBKv6+jFmhMVeH5aMu5bn4Oz9onu2Yo2aA1mPDWZmzj/mFeHdw9cg9Zgxl+GReLBcXGIC/RAHK8YBYBnpiSirqMX5yraEOqlwhuzB7CWPR5KGUQi4cvlJ55Lv76AC/bC9UBRI7RGC5aMFJ50h8X6YljssF99v70OO4lmqw0mB7/H6WkhmM5TPG7q0uP1PcW4cr0LI/r5sdNuq9WGu9aeY5PDcB8Vtt83gk2yPzlahtd3k8+8SAT8c146ZmaEol1rdGpIHCpuwssABkf74FiJUIjud0HxbteXM8U5QE3Mhy8SZdwzRKiIPmAuJXpFP9FkmpnyWEzO9jUWXtKY8xWw6wn6fw1IDf2RPGdv75RZ5JpRcZQSQmZf3DtaqB2i9uPEKH2igabL3HVyD7pe32lP7kppqpUyk67f9SRNtwFqsm5dDDx4nuLpzNVkl6lrJTbQUAdrSH5hc+oD4ODLZLkUMpAYA25+lLTzUbqfRPHchMw2+wOCXTuYuZp2MpuKiB3F7OsDQPwk+nFVoDuxnP7ELyHKz3XTEQAu1dL39dnvC3CqjJL7i9UdeOy7PPz0oHCFMr+2Aw2deoyM84NaLsVNKa6bhVkxvshy0O5ZMjIG+640sizJ+YPDkRKiwQcHr7HFOQDUd+qxt7AB8waHo6ZNh+8u1EAE4LYhEQj1UjkxITUqGcYnBuJAEedaMcshX2Vy36L6Lryz7yrqO/W4JS0Ey7Kdm5QGs0VAu293MYBp1xkxJiEAx54ai5OlLQjzVrFNjDatEeXNPegfqhGwtZ77oZAVPc6r6UBjlx4fLRiIpm49PjhUyp4jmroN+ODgNbw8s3+fxTkA+KidGxg3FJXHAeVKYtc4Qu5GTTdH9L+VYkDxTmIXDryTu67imHOOVrqfGIr8ot7QRWuH0/7JXeaqMPWJoRPt7qc4VtPAu4Cb36aYyh8SqXwo5rqCY0OSwdbF3OtquUpF8owP7XG6grsdswrEYMQjJD7H/x0gjSariSzSAhKByW/Q8yo1xAa4uouYVAlTuKm6mx/tnPNhMRPrlHnNNf9ig+A3wH91gb4sO5qdfsweFIavTlWyRXGIRomJyVxwdJzidutNMFlskIhteG13MdadroJaLsGKifFOtPei+i62OGewu7Aed9mteVyhX4C7U+IZH0hT2U+OlmFrTi183OR4YlIChkT7IDnYE1d44nSTUqhBEOPvjg9uFxaOm85Xs8U5QLvpR642sdN1V3hr71WW4mS1AW/uvYp5g4W0FJ3RjL9uzWfV2S/VdODNPVfxzjzaLcmtFk54bDYKpHMGhWFBVgQ2niN/9BH9fHGb/bGHRPu4tMD4XyEyXzvA7eFYzPQF726gRI1PSzfqKGDCRtfJ3Yge74jMxWRhkbue20H3CAIO3i+cVp/5mII2o8Br1JE3pURBBb/Km3weDT2c5Q4DpYamzQwUHkSZz1gI7OF5jveb2PfeEsCJ4TFTd59Yem4GzSUU3BixEFe75N32y0QiIPtJ+mFgtdI+ps1G9m98oRGjlqZGVjN5uit5yVB7Je09eQbTdY6CIn/iP4JIDKCPJphIBMwbFI7hsb4uE5w2rRGtPQa2OGewq6CeLdDLm3uwYnMeK3b09r4SxPq7Y4oLanqApxLfLR+GHoMZaplEQP8O9VLhL0Mj8bVd/M1TKcX9Y2iKWtOmY4tzBj/YlX4B2q9s1xqssPY4AAAgAElEQVSd7NCauvWoadMhNdRL0LT8y7AoAbV02oAQ+LorYDRbcbq8Fd5qmdMk7MGNuThXScehuKEbBrMFK2em4mRZi+DY1bT1Yv2ZKqyYGA+zxYqPDnFTA5sNeP/QNczMCIWnSoZAT4VAjI5Rl3/91lT8dVs+zla0CfY+bzg8nJvGLMxGShIV7tRc4yeQfDgWjwDF1OQZPGFIkTB5dZyAd9XSHmVEFsXVssM0HYkaQQlagMMe8KSVwMZ5tF4kd6fGJlPcj3+BitSOaqJU3vwmTd2/nMLtjRd8B+jfJ0ZUjQMbqqWEmqBqH2pApM6h+MU8fl0OsZQCkohhJRbTGhFf9fj6RWIM3PQKWV4y1FeAGgaMxeTIR4EdK7jrhj3AxUGpHMi82/Ux/yVYf8e/n/8PYP2F45UVTYW0I8Mlv7YTRrOVjTFPb8tn96793BXYcu8wRNtjk95kwfozVSiq78boBH+XtmdBGiUOPDYa5yva4OuuYBmNjna7AOl9NHTqMf3DE6wr0Pqz1dj7aLbLSfx789Px+fFyFNV3ITveH3cMcWaS6E0W3LnmHFrs0/XL17vgppCy+XBtuw6PfJuHnKp2xAe6Y9W8dPQP1WBmRii+PlUFo31NwNdNjnGJlL9qVDLButLWnFo8+30BjGYrfN3kWHv3YDbm7ikU5hx7L9PvrT1GJ7G6xi4D1HIp/NwV7Ot1BF+I+XdBYxEXMwBaF9S1cboYfSE6m34cEehCRDUgmROb48PxsoSbadLMMB2TbgGSZ9Lg5RxPkPfCGmq2Zj9JOeuVn0iNfsobQktHgArunx+loZRnMDDlTY6BadQKmwYAiYGKRMDctbTe2XiZnstxgDRyBa1iVp8lGj0jwCx3Iy90V5CrKSbz0dtOuTQDs4F0Rgq2UH4+6RWK5b/j6sN/dTbb1M3tBkf7ueHnh0ZiW04tlDIxbhscIfBrnDMoTKDaPnVACFRyCX7Mq8NndquMXjvVfHCUDxscASBYo4RMIhLQI6N4U2OL1YbtF2tRWNeJYbF+mNw/CNF+bvjr5ES8e6AEepMVo+P9cfeIaGzLqWUnKwBw99pzOPX0eHxxVybe3nsVpc1kp/HgWBdUPztcDcdsoAD7wo+XsauwHmHearw4PZnt0nbxCnqAGhRWq02QLF/v6GWLcwYlPCuMIdE+2HCW27cXi2hnHyDLoAfH9UOv0SKwXAN+WYDld8WGObR3GD1KSN8+8CKweDdRzfVdwOfjOPqMbz/gnkOufSvFEtrRYbzEGWgdp2A2mr54R1LA/nwsN/kJyQAW76WdQYU7nDD+eWDrEnvSJwLG/50uH3ovFdTX9hHVdAgv8e2so9fLfzy/OODe4+RrqfCgvXuZiqidG+aQ+qVEDox7HhjxMCWd+1/kKK1iGU2yXMGkJ8sfpjMZOgi4awcFUUMPHc8Wu3jdkTdod8nNlwSavprK7Xj2m+hsy/Qn/iM4ikICNJM79MQYyKVidrdvaIwvwn1UqGmjz0AkAmYPDIOHUgaNSiZoBIZ5c/TE85VtcNSiPFPeiimpwdhyoQbvHbwGvcmKRcMjWRqjK/9cAPjHjP64dSA5Uozq58/SxDVqGRRSMQy8YjXQk5LPL46X4829V2E0W5EW7oUv78qEr7sCa09W4JWdJKwZ5KnE+qVDWMu1eZnhCNGocKi4CfGB7pg9KAxNXXrM+eQ024CdNiAYH95BFOYuvYktzhkcuNKElTOBrl7nk3yXno6VDYDBYafVYO9QSsQivD57AB7/7hLatEZE+7nhebv4ZoiXCuuWZMFosiD++T0uj9UNgeOONYMzq+2rNFr6Xs74iBOuLDtk30sc5ixaxMfsNaTn0VpKyRx/L9vbYcojkhAFvWQfxWcm6Rz+MDDpZefHDuoPPJJP9pfekUK/dP944OE80vLQhNOEv61cKOoGAHkbqEAPHyK0HvKL5xJtgL4YTHFesBXYthRsm3nwUmoOtFcKhfQAjt457nmKs4Yu6p5NeIGb+mQuJpG7imN0PmDcQ/jNEQb6LqLL1+cTBdRxmv8n/iM0dLn2Rp46IBjP2/23B0V6C5TZB4Rp2OK5tKlHIIrW0mPAZ8fK8NqtAwAAj36bhz32gnPbxVo0deldCp3JJGIMdxDonDMoDN+crmJXHRODPHBTShC+OV0psOxt0xqxM/86Fo2IRnkzvR6RCFgwJBIRvmo8OiFe8LhVrVpcvt6FzEhvBHgqkV/b6VTsHihqZAv0538oZJsUJY09eOTbXBx8fAxSQjTYcu8wfHu+GiqZFIt4dmp8GMwWvPTzZbb52Ko14vXdxdh4z1AAxOjiuwZF2OnwiUEeTo5CszJCIRGLsHJmCh7/7pKTGj0AWG+oU5AL2AykJzH6KXJ02L4M6KwhzYl539D6jsUE7P0bkL+ZBiqTXuGGRgAVlTYbFcchGbT2c2IVxYGUWSTSJpYSO+e6XSFfquJYSwzEEmD2F5Qv2qwc1b7pivPrbioCkqZRfJ/xUd/v78S7QL6dCdRRTRPzx65QUSxTC/e/AWquAvQ+7jvZ9+Qd6LtJwYfFRPZuHVUkcsxQ+uvzgW1LqAHqn0QipIHJNBhjrI17GkkvJHoUIFcB+j7Oeb8x/qsL9Ib2Xnx2rAwpIRqM6OeHaD831rfWEY9OiEOwRomTZa1ICfHEIvv0+6KLvb/c6nZBge7rrsDfbk7Cq7vJ7iwuwB0Pj+fo48//WIiN9sL169NVeGZKIpaPjsW9o2OxcGgkdAYzu899xIHCqDVacK6yDROTA7HqNhdqry5w+xASpmOS54RAD4xNCMD7B69h8wU6SRTVd+He9Tk4/cx4KGVkKbT6CGcJd9vgcCchoxg/d0T4qAVsgTEJXPfvlrQQXGvswXp2Bz2e7RADYAWnGDR16VHa1OOUyP/vwUbdNKWnkL5t0lIyOusTUoPk7720llJCNngJJao7n6DJj080UYNcee2m3yF8/IAUjj6fu15Iy7yeS9N6RvVS20pUd69woh2lzKKCt+YcPQZ/rzF1jrCLqGsDvl1ASajMjShCjP0ZQBQnR5rV8VWcNYXFyPlcakJpX/zMaqJrDr6HbOO6rtPxkKuB1Hl0LIt+EtKG6nJob3Kg3fOcKc4BoLOagvywB4Czq4XFQel+bpf9T/yP4MoX1gYIvq8ATWe2LB+OL09WoKXHgNkDw1gF93/ckoKnt+dDb7IiwEMhEFRLDXXeuRwQ5oWrDd14als+20R8e18J4gM9WHZPfWcvXvzpMgpqOzE0xhd/n54ML7UcaeFeSAsXPqanUoYnb0rAq7uKYLXR/veKifGo7+zFa7uL2anKpZoOrD5ShofGx+H13cWssGZDlx6r9pfg4wWcZeHIOD+BYObaU5WCeLcjvx6LR7ZjYIQ33OVSBHkqBck7M+0ek+AvuE4uEWO23dVCJhFjQVaEwHZuEY9tNTYhAKefGYemLgPCvFVOFFKd/nee/Hi59jDGHl6sKPiOqIlZy0mA7dBK7rqJL1NTr7eDqOCVJyiBnPwaJaGMUI8jsp8Aqk+TrgbTHPQMJmsd/kTozGq6rVJDE3ZG7Tx6FE2a+cJHTUVUxIYNpiSVvyOp0Djvk6vtNOOb3yIBtrJD9h30D+ny9irabQ8awD3Pqfch4IDlfEWJcGgmTcp7OCoxEm6mfyOyyJmj5hw1VB1FlcKHCJsXZz4ha0/H5sj2e2gKBpB6va4Piu+NFXn5/w6eCtd06I/u4IS3Xp2Viie3XsLZcm4HnUFnrwuat5b+zjp0Ruy9IpwOf3u+BktHxUBrMOPVXUU4UdqC5GBPPDct2Um13NddgT2PjsLey42QS8WYlBwIpUwClQsHDrVcitp2HWZ8eJIdtmw+X4N9K7JZy0oAWHemCn//sRA2m91ZaOEgpIR4QioWCYSJ+/GGLfm1QjHIsmYttAYz3BRSl/EboBzQXSmFWi6F1mARUPUBGgoxeGlGf9y7PgdtWiO81DKsnEnfN5FIhHVLsvDp0TLUtOtwc2owuyo6uX8wRvTzQ+qL++CILp3ryfoNRXc9FaLf30vFOUCrNrufAu7cTrayjCq6vgP47k6uyD3yBgmmWc1khTb5dWDMXylXshiFDcNFO6jI17ZS7ugbC/Q0A3nrqZBNm08Fs6PmRux4WsFh45eIawj+GmrPC3836YDGK8RwEokoZm5bSpN4nxiasPPBj0lWCw2L6u376olTuevyNtljLIARj3KCepvvJOYpQMdq4TZqDv/0IMdOai4Cfn6YtADqcoTPbzWR8Jzo99Mm+K8u0Nee4TqW94+JFexHdutN2JlfD4vNhmmpIdCoZZg/JMJJZGhwtA9LrwTobygzyhuOWDQiGjMzQtHYZUB8oDubVOlNFmy5ILST+OZ0FZbb94DcFVLB5CgxyAM/86z6RCKO+s7Hxep2fH2qEmKRCHePiBJQLyN93bD30Wz8fOk63JVS3JIWArlU7KQA3K4zoaSxGwPCvPDUTQmI8XPDuYo2pEd4Yb5djTO3uh3lzVqMivNDgKcSXy4ajNd2FaGihURGHuKJeIhEIjxxU4JTE8RsscJiswn2kjafr8bfvi90afn2vwqPYKK3O4LZjTS56KQzKu5HXgMu2b3UG/KBzQuAFVec7dBSZhF9/fJ22jkf+gAXnIxaOIG5rKEAWHsz52+bvhCY+ZFzoDX10klArhY+zolV3ITIpKUEO3Ga0F7IEW1lwt9tFupQakIpIZ3J66i2V5L/JGNBd/Yz2gXSukgSGRaBqz13s/3E6bifCuAP1M35/xo1baSkPjDSC0EaJeszy8fMjFCMTQhAVZsWiUGeAqplcognXpiejHcPXIPeZMHtQyIwKyMUG+xrLnycq2hjC/RHNuWxU+ntuXUwWKyCJLipW4/jJS2I8XdDRoQ3lo6KwZTUYFS2aDEwwhsquQRnyludKI/lLVq0aY2CaTsA1HW4nowxaHNBg2QuE4tFeH12Kh6zT7sjfNT4+3SaprkppPj+geFYd7oKPQYz5g4KR/9QjmXz92nJyIjwRkFtB4bF+rKUTwYKqYQVTNKbLPj6VCWuNnZjTEIAxvcT7qbecPC9Y3/xdvb1gFMO+hKn3qcCfefjnKBZeyUVj3yldIuJdtj1nUDizVS833uCJuDugVwC6jjRt1koZl9cR8kYgzHPUgLLYPsySloBmkjf9bMwqXXzJeeOo6/T70oNTbsAou8v4AlfAtQ4/e4vXLOAaUT0NZmTKYE7f6DzRNd1opjy/YiVnsJpWdkhoskHplCzk6G1t1wTri8VfEdN2owFdPz4KNgGlzC4OM/8iT5R0/7r7jghXipsWDrU5XUZ4d6ID3RHid3iUSQC5g2mxpdCKoFSKhFoYHjZJ8wv77jCTt6rWnW43qnHjw84a+R4KGWYM0jYSJuRHoKvTlawAm0JgR6YOiAY35yuEjAhO3Qm7MqvxyK7w5DZYsXbe6+ycdpotuKtvVex65FReH5aMl7fXYxekwUDI7xwP4/JOTTGFzsLOFee1FCNQJyYj85eE+5bn4NTZa1QyyX46+RE3DU8CiP6+bI2nQAEeh9Don1w+plxKGvSIsbfTbCf7u+hwHPTXFC+7cfGFYy/s4g7APrOG7oph+KjsZD+rXRwXjDpaEgjVQFHXuUuP/cpNfX6z3bNrJS7EfOGgb4T+GwMrQkBwOmPKLY6NgJD0mnCfOp9yiGHPyRUfweoyXnhS2L6ZC7hxJQjh1FTkH0N7sIGaMxoajZ0Xad8VSSi3Lb8COXd/OfZ9STnNnT2E85arfYC8APPovj75cQ8UHhyxTlAxfbZT+g5mfMSA0b9PXKkcI1KqqI4avp9pufAf3mBzseaExV4eHwclDIJuvQmTP/gBCu28fHhMux4aCS83Zx9pacNCEFJYw/Wna6EWi7Fionx6BfggbPlrfjsWDlMVhvuHh6FsYkB8FLL4aUWPoZELIJSKoGJV/S5Kfru0Nw9Igq51e04UNQEN7kEj01KcBJZK2vuwfzPzrBUoN2F9di/YrRAATNIo8Q92UKKVHqEl4CS6aGQslZIIpEIczPDMTeT+8K+tqsIn9rp/UqZGN8szsKQaB+sWST0wNabLLhU04EYf3en/aY1JyrsNH4L5maG4+UZ/WG2WrFyZ9Efrzj3TwKGLKNELWIYTW8Amt4wk+b+s4Hjb3MFptqPswmrcqBHaptp2h6YQnvlXXW02y0WUwKaeLPza0i7jYInQx13DyJ6EUAUIqY4B6gbOuox4dT84Et0f5uVuqxT3uCK/2YHuxGrmQpw90AgZy2JmIQMpCkYY8OTOE047fcIpiDmChe/4YpzgCbjJXtINO/wq9x7kqlJVRMgEaajb5KoHEDiI4yN0pDlFECZIj46m7MR+hM3DO8duIZ3D5bAZiPK+MZ7hjpZpjHQqGUYoOaag9WtOmhUMmjUMtw9Ihp3DYuC1WZjxTYzXExR0u3eugazxYkyfoK3x32+sg13rjnLinneMyoaf5tKEyX+VCk93Av+HgqBb/mk5EBE+7khNVSDgjpu0jN9ADWnrjV2492D19DcZcDMjFDWwePWgaHYklPD9oWCNUrBhH2Mfdrd0KlHuLdawDoK1qgETWE+RCIRbkkLEeyanilvxXcXaqBRybB4RDQbzx/elIt9V2jquv1iHR6b8Osev78p5H0IZEnkwgYbq77uUKCK7I0bRmSNQcUxomlL5TQx+foWroF4IAC45yAlcgEOzaEhy4EfedTt/nMoZjMWmQxOvU8Ft0RKPuJMcQ5QQnx+DTD6SeF9xj5DE6fWMpr8uFpdYnD4NeEk/+ibQNa9JGK0dTHYKVTmYu5xApOB2xxE26pOUxyOHc81Sy98Kdw7rzwBzPyY/u9K4K0hH5AupYYDX4VYE8ol5Xw4WtP9iV9GHz2XHfnXsfdyI6J91VgyMkbg0sCHWCzCpnuG4qtTlWjo1GNGeigbR1RyCVZMjMOru2i1USkT47GJVPQcdWBUXqrpQKfOBIiAlTuu4ExFKwaEeuHv05NZsV4GnkoZdj48CgeKGiEWiTAuMQBKmQTuSueywJ1XxJqtNicB4w4dfc/vGh6F2YPC0KEzClaaAOClGSmw2mw4WdqC1DANXplJxVlxQxd+yrsOfw8F5maGw10hxadHy1hBPZ3Rgpd2XMGklEB8fMcgfHj4Goobutm1Tz4UUgmSedaW5yvb0NRlQHa8n6AQP1veip0F9QjWqLBgqGtnht89+wxI4QQswwYLJ8797I25sExhoSlREDun0EWjrT5fuPJn1BIDVNdK+RVfCK7oZ2Ec0HcQvZtpQPLR/1ahTzkf7VXAFxM4raK8TcADZ0gYdPjDtD7JuG7c9KpQTwigNSBGa6m1jMSSGUZRxp0kGmfSUy7Jx7nPqEAvO+zwgmxU4CfNcPFi7V/amDHkvsEgZgz9O+QeYjFc2gS4BZBGlNqHmhvmHvweuCEFutVqxYsvvoirV69CLpdj5cqViIzkhNOOHj2Kjz76CDabDSkpKXjhhRfw+eef4/hxslrp6upCS0sLTp78BZ/O3xg2G7ebvTO/XuAXXtfRi+9z67B4ZDRaewz4+lQlGrsMmJEeguH9/PDYxHg2YAI0Xbrzy3NsgXziWjN+fGAkUsPoJNzQqcf1zl4MCNVAJhHj4fFxeGVXEQAq2JldH6PZild3FWFH/nWEeKnw3NRkDIn2wRd3DUab1giVTCLw0mWwu6BeIBKkN1mx93IDu7NktljZhJiPh8fHobZdhz2FDQjxUuHlmf377HC2aY2sIjvzHB8cuoZ1S4SU7fzaDixaex5tWiNkErKVu80+fS9u6MLLO7idlo1nq5Ee5oVJKYFOqvR/CNx3ihMvW7idErruBioimSTR3Z+mwrkbANiA9AWAh336FZoppM2ovInKc+VHEsEwdNHvd3xHO98MbDbq2snVdP3yo6S8K1HQhIURtjC6CBr8iXv5Uc5yDaAua9QIEmICSNH3Gm/CovajYvvQSyRWBNAOT+Nl4FY7zWrQXTTRLthCQXjss1S822ykmtxVT2Jybr5cIs6HSEwsgaX7KcjarLQPxZw8VN70fvM2UrKbdjvngRyRRZ/JlR8Az1A6GR163eVH90fG/6V42dStx/uHrrGxsrHLgA8PleKft6Ujt7odL/58BZUtWkxKDsQ/ZqRAbadRdupMWPL1eVyoaodcIsYjE+LwwNh+EItFEPOy2/6hGvx9WjLeO3gNRrMVfxkWydoIKaQSxPq7CSx5koK5VaIPDpUK/My/PFmJe0fHwtdd2BRUyiRYvyQLq/ZfZZNhhhn15aLB+OhwKcrtDKCFWRE04f/8LLtfea6yDSq5GLMywpAV44sNS4diS04NvNVyLB4Z7eQDr5BK2CZqu9aIH/LqYLbYMCMjREAb/ehwKdafqYKbQorHJsYLxJEuVLZhwRdn2cn/zvx6HHlyDPQmK1ucM/j+Yh1+V4SkAbkuLp+/kajs+g5S+2XWaUauAA68wN1upL3QDEgR7nj7xnHsovIjwuu0TVRAT/wH/W4xUcyRymlSrAmjxmFAEjDATnF0FEKyWsCm4UwDkI/ueufLAKKYMxZx3Y3UkG0tI5pl5mIe26lbeD9zL70GhlJaepBEm+Jvcv08ALD7rzTpAWiCc+f3NIk6+5nwdpe+pZUApYZENh2bIzFjqBEx+Q3gxwdo7UjpRVP9LyfBCX35Ev9B8EeLmX3lSQ9u5L4YJ8tase0+KsCKG7rw2q5i1LTpMCU1CCsmxMPX3dmbnMGy7FiMSwxESWM3hkT7wM8e0xKCPFDfybF8QjRKeCileGRzHn6+RH/TNW29aNUa8O0y5/UvpYxsdvmYmR6Cdacr2Wl+/1DOyo25z4y0EIF7x208f3NH1qfOaIZaLoWvuwKrFwqb9zlV7bj9szOsQNz2i3X48YERAltNgHSaypq0GBnnh79NFU7Cc6vbsSWnFh5KKe4aFsX6oT/6bS5+yKNj4OMmx9Z7hyHG3x2Hihux5OsL7DmM2e13xC/MyW4M3HksqTlricHYkA9Ej6ZiFgCGPWhfm9xCRePk10hLKGoU5VJ8ViFf28NqIb2e6/a/x2PvUM4VmEK/S5yFAdkmXd4mWkuymIBh9wND7+v7PVzeLsxDDZ3A5R/oflIFMP1d+uHDqKP79XYQe5QRXD71vnDdJ3cdFeE+MYBUCRh5q0Yy+5DSlU97YCpN0ROncV7yEjkn3DzrE5rI154nkbkpb9HlYgkJdN70isNx+T9OcT9w4ACMRiM2b96MvLw8vP7661i9ejUAoKenB2+99Ra++eYb+Pj44PPPP0d7ezuWLVuGZcuWAQCWL1+OJ5988pee4jfHHVkRbLHrSH8EAKvNBovVhvmfnWGDx3c5NVi7aLDAZgwADhY1Cgpkqw3Yd6UBqWEafHS4FKv2l8BitSHCR40NS7NwT3YMhvfzRWFdJ7KifVlV4c+OleGrU5UASFHynm8u4Mwz46GSS+DDm+ZXtWqRX9uJQZHeCPFSOXVKASDQU4lOnQmPb7mEg8WNCPdW45VZ/VnfdYAC68cLBsFitUHCm/R8d74GuwrrEe6txgNj+yFIo4TeZHGacGsNzkX1G3uKWQV8k8WGlTuLMCM9FEqZBAUOO0kAkF/XYbdv8xcIqvwhwFcWl6v7Vsf1DHGevABkD9ZVR+rv3lHA1FUUVH9+hJt8t5UD+54H7rCLaZQdBn56mHavo0eTaJJvLHlEOmLQ3XYKo/1zCRtM9kYMGOoOHw0FXIGeuYToVQVb6T2Me46EiPI2Cu9TuJVs0JjkOWsZ/fCxbQnX1VVoSESPUYJnJjgBKVxyGpAktPngwz3A2fqCgV+cUA1e5ub6dn9g/F+Kly3dzoq4DZ16GM1WLFuXw06lt+TUwkstY5Opz46XsYrqRosVb++7iukDQlhvXz4Wj4zG3SOiYLPBSefi7blpeOTbPFS36ZAQ6IGVMzmKnGP8sVht0JutqG3X4W/fFyK3uh1Don3w6qxUJAR54NM7HexaQFTIF29JEVyWU9XuJH60p7ABszKIMko2bUJa+dacWuzIv45QLxUeGNsPIV4qdOlNmPbBCVas6dNj5dj18EgEeCqxM78eb+3ltBYe2pSL/iEa9vhsz60THPembgOOlTQjO94fKpmQ/urtrkJF2++4P+lIx2TgSpkdoO9yWCYnEhdpLxymvkOU8NZrJMzGFxwy/8Lq0KGVdlaQjZLHCS9QcuooPjfsIWA373uStYwTbus3gdg5vXaGhkgs1OewWoGKI5Skxo7j7rdhDjexLjtIRTGTwGYuIV9fBmnzubWi4DT6YZC7gXblpXKa6idOpeKfr5hs7qUGa+RWZ4VkiYzEnwCi/jPNkd52aqIOmEfXDZhLr7/lKj2/vI94qXc+N/+R8EeLmcEOe9+ukFPVjooWLcK8Vbh77Xm2sP7ocBnUcikecBD2PV3WivrOXoxJCICPmxz9AtxZDQsGL0xPQX1HDq42diPQU4G356ZBLBY52S2eKW+DwWxBZ68Jf/u+EGfLW5EW7oVXZ6UKmJUAUb53PDQKR0uaIRED2XH+TgOd12cPQP9QDQrrOjG8nx9mD3R2cihp7MbDm3JR3NCNpGBPvD8/3clCc8NZTr0dAArqOnGhqh1jEwKwn9d49FLLMDDSmV11qaYDcz85zeaiP+Zex6EnRqO6TccW5wANlD4/XoHXbk3FxrM1gjWqSzUdjg8LAEgO7ds674aglbcu6BUOzN/gfBuZkgrKGR8J3XoCk4FbP7cX0kYg6z6hlWXlCa44B2iF8fwXXM6VNI3yMcZa0jOMhksNBSSOxuSUe56m5mTsOFqlOfMxDY8GLaLiVuW83itYE3KExQysncytSR19k0SV/fpR7HKEro2ef/STwH674LFIwq0qxU8m4cvzXwAQ0RQ83t6AnPs1UPwzCdElTKXnACheOrKW+kJrGdDd9Ou3+41wQwr0nJwcjBpFUvfp6ekoLCxkr8vNzUV8fD74qY4AACAASURBVDzeeOMN1NTUYO7cufDx4T7Affv2wdPTEyNHjnR63N8aT0+Jh85gRXKIp8BvctqAYHx8uBTX7QHU30OBGemhuFjdLujs2WzAlgu1TgW6q6Qz3EeNJrvoEJNoVbfp8MGha3hzThpSQjRICRHS5U6Xtwp+7+w14Up9JwZFcsdr8/lqPLO9AFYbIBWL8MHtGZieFoJtF2txppySjVFxpAz/8o4rrJdldZsOD23KxRm7CBwf/OJ849lqPPs9V9idrWjF3kezEeKlwrjEABwq5v5YF2QJ7eUACLq7ANCtN6PHYIZSJkFWtC8kYpEg8RweS7SuD27PwKdHydbjxLVmGP7XpdzFQFMx0Yt8YqgbxwRIq5UsIsxGSgr5Prw2GxXfSg39zN9At2eK/a5650DEKPeajSSawQj5VBylyRNDZXREwmRg0U6aKGvCKWjyETMaROvhHcuYMdz/RSKaZo1cIbyf2k/YyVR6ccmgKzQVCSlXhk7g1IfArNXA/WdoCi9T0yRJ6qJzy0dPMwV4/snIpKfpmERGiTVzvJuKgYLNrh/nD4w/YrxUSACDg7CtCDSxdqWIW9mqFVDGAQhs1ipahDutNhtQ0apFhK8aWy7UYNvFWvh7KPHwuH6IC/SASCRiB5EmixVWu0ZFRoQ3jj45Bu06k6BJCQALsiIEVkZjEvwR6qXCvE9Ps/oaB4qaYLHmY+3dQwT3vdrQjfLmHgyL9XVaQ4rwUTtZXjquFfGx5UINnuTZsZ0ua8WBx0Zjd0E9W5wDpNS8PbcO946OxelyoQ6DxWrD2YpW9lzi62K9ytddAbVcikcnxOE1u7OHSibB4xPjsWDNOafb3zD8q5ZcRi2JrEnl5M0d5fA3G5hM3uE9jYCbv/A7328Crf8wmhdSFTX8Sg9SUsrgxCqiibpqDGQtI6u1iuMkEsdfIVJqgCX7aGqj76LHZuimZiPw9XROyNI/kW7b0+RMJy/YyhXoIx6mRmz5EWqUpi90fVwqTwgp+ZvvpDgpVTjrbDArPNlPCvfbhz8kLLb7ao4AxGZyY95bH40cx+n/Hwx/uJj5L6zjySQiaFQyXG3odsqLjlxtEhToj32Xh+12JoyHQorNy4ex1G2j2Qqd0QwvtRzRfm7YuyIbTV16+LjJ2UI6IchDoCkU4+8GhVSCZ7fnsjng8WsteHRzHjvVB8CKtsmlYkxM5ia6l2o68PGRUmgNFiwcGoHJ/YOxeKQLr2wentqaz54niuq78NS2fHx/v3A/nq87xF0mxu1DwtHZa8IPuXUI8FTgyZsSWDYWH9su1goGRQ1dehy92gw/F1ZxjEuGhwsKvyv0Gn9nq0GVH4mXRQ5zViS3mKkhyeyTi11McR1Ff/noi73IQKailaGiHcSuSZpOMbFwK5zI/hXHgOB0YM1ELnct2Er3T51LQxim4A7NJHs2ALieR5Tx6NEctb38sFDDxNBJ65Q3vULx8spP3PP7J3JCmCMeoWNUf4l2xZliWyQiVsG45wCIhDpLEmnfTkJ8WK30unrbaYDEuHscfZNWMX/H5YcbUqD39PTA3Z3r9EkkEpjNZkilUrS3t+Ps2bP44YcfoFarsWDBAqSnpyM6mr7sn376KVatWtXXQwtgMBhQVFT0H79Od1MXRoerAXSguFjYRVs1ORCHyntgtdkwLsYdLbXlaG13FgSyGnqcXkOgzYbxMe44WE7F/JAwNRKU3Th9yVmc6Fpda5/vIUQpnAgppCJYO66jSEcB1maz4dWdVey5wWy1YeXPBYiShuOFUV4oSVJBLAL6+SpQWnIVZ0qEVJ4OnQmHzxciyluOZq0Z52t1CPaQIj2YUwb+9rSQ+lfS2INdpy4hxkeBhwepEe/pi9pOE4aGq5Gs7nZ6L8ND5CjnUVIzglVorikH0999Otsf63Lb0Wu2YlqCBtHSDhQV0WcxNQKYGuGOS1XNMDjoMoiA/9Fn/0tIAOAYzqwARKuHQ2S3wemMmITrw14CrGZEHHkIbs3UndR7xqBq/Kewyj2gailAyNl/QN5Ti17vRNQNfwUmd+dOc5R3ElTt3Htp8c9Cc1ERZN016OegsquvOIMK+/v2qD0Cv8LPITFp0R47C63JdwHwAaLt4h8VdQD4dFcpPIe+CN+ibyCymtGWcDs6en2BoiKIzL2wiaTC5oIdbglLENb6DMQWA2wiCer734fOq1ch1TVBU7mLjkf0VJhVxMZQtl6G46m7q7Uedczn5Wk/+ZRVQ9LbCt+rGyDTNaIzYhJ6wmjqJeupRdjJZ6HsKIFJFYDrWX+HLjATYmM3og4shaKbpna93omoGv8pbCIpYnfNhVzrTFW1ASi+QX8rvwX+iPFSJRU5NcXkYqC4uBjPZ3tja6EYDd0mjIxyQ6p7D3qbu+ChEKPbwCU14W429vmSNRbs4j2Wp0IMT30TPtlVidePcU2+41cbsXZ2OJR2QbnNBe3YnN8Bk9WGm+M9sXyIL8T22CQkdgNJKmDlhCCcqdEh1FOGKfFuuHzlipP45amyFsFxWJ/Xhg2XKOaoZCK8OjEYif7CCeWd6d7YeKkdZisQ5yvH2GALioqKcLi8Bzl1OkR5yzE90RMKqRibTgmp0eUtWvx0Mg8N7UKrSgBobmpCUZERfmJn7QQ3YxuKiugcMszPjO2eMtR10WOMinSDe28jiooakR0ARM8MQ1WHEf0DVVDrHY8M4UbFS7/mBrhy6m3c/izaku4ELCYEX3gNmqq9sEqVaO6/DO3xt7G3E5u0EJu0MKv5jW7n6Ykk+2NoKn6GxNiNzqgpMLaJ4XtlPwIcbtd8aR9azH0oy8MfCL6VgoKr4xFnL5RN3PUe1QcQxneZaC5Gw95/ojNqCuLEcoitXF7QJdZwcQ4ARHFArH1dqeQaNBW74Fv0FURWC9oSbkd73Bz4X/oWAmMsmwUNpzahPX4ewoOHwb3+NHtVXchkdBUVAYiBbPImuDWeh8GrH3r9BtDrtVogtuhhdWASaSp2wv36CRg9o9CasABWuTtEJh2iDt4DZ74doDOYUfVnzPyXY2Zpq2vhKH5MvK2/Fxqry6A3WqGQimAwc/HVX25mn6e208gW5wDQbTDjrR25eGpUAA6VdeOT863oNliREazCM6MD4GHnYvPHOYsHqFHf1o2aThMC3KR4MFODoqIinCoVTv9yqtpRcPkKytoMePt4M2q7TEjwU+Dp7AAEeVAu0NFrweLt1ei1v94TpS1446ZgDAjiWANlbQYUNRmQHKBAjA8Vx/m1wpy6oLbD6ViODrbiJ5kYWvtqUmaoCvKeehQX12NMIDBmsj2ydNfjUmEdDpb1oKHbjOERaiT4K2HSOjM9OpvrESFRIspbjkp7zi4WAcMD6Xw0MVyE/ZfF6LEX4BP7uWN/qfN6YEOH9obFzES4kC1oyKEfAI1pD6EtkdwrPCt3IzD3XUiMXegJzUZd1guwydSQ6NvgW7wesp46dIeNRVfUZMHDicx62EQSyulsPoj0TYW6lYZtFqkalb7jYXR8f7L+gAxAxXUA16EyeiPK4WXWWv0hPvIlQviDJasJrUc+RVP6Q8DIj+HWdAE2iKALzARKKxB48R34XCMRTbNcg6pxq2HUxMCtvhqOCgCtrS1oKioCEA716Pegqd4Hs8ofbXFzYbnK10lSAuosoNlE6ut9wWqGTNcIkzqIN1SzwL9gNTRVe2FW+qIp7UF6rTYbwo+tgHsDxXuT0g+VE76ATaJA3NE3IOqjOL9ROeYNKdDd3d2h1XJFmdVqhVRKT+Xl5YXU1FT4+9MXLzMzE0VFRYiOjkZpaSk8PT0Fu0S/BIVCgaQkZ/Vg1yh3vr/GH0lJ9OdR2aJFcQPt9zCTmaEZwtsnAZhTR/RFAPBzV+CvtwxiKel8rEmmXXSL1cZen2q14b2zHSjnTZPmD++HpKQIGM1WfHykFKfKWpEaqsEjE+LwQkwcOq152F/UCH93Bf5xSwoG83aBLFYbek1CeqHeImKPieORGV1uQ8lxbm/c30OB8UNSkV/bgXs2nGUVjG/LDMcbc4gaHZtvQH4Dd7KQikUYMiCJFXtL54kwAjTtKqjrRJi3CgEeSryUYENMeCWOljQjMcgD94/pJxBKSUoC7hHGFSdYRNUAhM0KEfBvfPb/JlT+QK+QIiaWewJG7oSjqd4Hza2ryOahmaMOKbvKkaC7AAy4D/jgDqCH/lZU7cXod/UTYOFW5+cL3U7ibU1XgLhJ8Mt+Cn5SOWCNB04JvSGViZPofbdXAVueYycoAQWrEZAwRCje0XgFOPQydS37zybLiaQkYDLRxYMBBJsNtP9euI1UNcc950xXT0oChs4Aas9DFJSKEE0YQnqagNXTWTG8gPLttA/u7g/YEoErH3E+myIxPMc+As9Yh8/LagU+HsraqHnWHATmfkVdzo0vAh0UjGW9TYi8+DrwaD5RPru5v3lVezESrddIDdRFcQ78+38rN+qk3Bf+iPFSIq0BDA4FpViExMREiEQijBjofJ+PFQH42/eFqGnXYVxCAF6dl85+15OSAI1vJbZdrIOfuwKPTohD/1ANvii4KHiMDr0F3YoAZMT742J1O766yMXtn4q7MCEjlhVOy6/twKr9Jay9290jopGUBDjOKVND2wTCb+nh3uxx6NAZ8d16Lib2mmz4scyEWdnC4P9SEvDIVAPadUbWF/3To2V48ziX8F43yLF64SDEXTbh4nXOmUMiFmHogCRMUErxQ8kJlk0Q5KnEfVMGws9dgfgEG5othdhyoRZKmRiPTojHzcOFba4jaSk4U94KjUomcOUAhLHeYLQAcKad37B4KZkLlDozVwKvbUTgra/SvrS9kScxaRGU+08EDb+dqIon36OphFlPE5Hb1tPkpquedqUZS7RbPgD8hwLppILNFrTus4GC1YLn9R8yG/4R9vfaUQ0cfYPiZcpMod9vwVZi4vgnkvBlX3Rv7Rmni4I8FQhKywJ0/yAau9UMeITA85bX4Onveo8Y9ZeAc5xFUdDFtxHUPxtIzgaKhRTLoAHjEBSdBPT7ntTn22jHPTQ6G1yLNwkAb3/86m5g56NATwMdyzlraT/1zGr78xL8dKVks3RhLdDp4MJhhzpp4p8xE/96zAzo1gM7nDULTj87EecqWhHp6yYQ0nzT5oW//3gZnb0mZEZ646V5g9i9cktdJwChcJ9E4YagiFi8v+Egm6fl1vdiX60Yz01zfn1JAG4aakNzjwF+bgp2VSgjslOggp4U7InUlGQ88s4R1Nqbf1dbDPiqUI+vF1MOuC2nFr1mYTy50iXHbWPpededqcLzP3Nx+tVZqbgjKwLDY7twopQbMAzv5+90LJMADOqfiAP2HHdsYoCAwcnHorXncOQqPd7Wyx344q5MrJgejRO1p1DTRg2ScYkBuG1sBkQiEb6P7Id1Z6rQ2EU6I0OifdjnHJ6ehKMlzQjxUmFwlA+int7p9HyBXuobFzN/BYGl3yJw1kpi6Wx5lbV29Kg7isSWPaT18+kodm3Rs+4oQgN9ifljMQM7HiVxM6mS2DYjHwXiDpDeka4VkuQZiNWEkpBczlpiNg1Zxk2iGSQlAaglDSKrGRi8FGET7ycrXweSlm9EInyZ45XC2wVvrwKucXmv1NiJ2Os/AkM/A+LjgJKvOCaSwhO+k56AL6PBlJQEgJws/ABaMao4SquMEUM5vY/2Kppwd1RR/jh4CV1ecZzWLXsaSVD0tvW02nNmNVC8HgAg621G5KmnSUG+oQBo4OK9TN+CuPbDNM131DDhQSSS3ZB4eUMK9IEDB+Lw4cO4+eabkZeXh/h4TkAtJSUFJSUlaGtrg6enJy5duoR582g/6tSpU8jO/hWz+d8QZU1Ev/nyRAVe3nkFNhtRBNcsymSp1o54e24aFg6NRGOXHiP7+cFNIcWFyjb84+crqG7T4ebUILwwPQVKmcRpt0csFmH90ix8eLgUte29mJYajHmDSYzllZ1XWLu2cxVtqGzRYs2iwfjsL5nQmyyQS8RO+5gSsQhzM8Ow4SxXwM0f0re4y4qJ8WjXmbDvcgOi/Nzw4i0pkEvF+ORomcBe6LucGjwyIQ4hXio8Mj4O5yraUNfRC4lYhMcmxTspsTMoberBnWvOor5TD5lEhL/dnIRFI6KxeGT0r9KhGFyobMOGs9VQyiRYMjIa/QLcXWoC3FCSyR0bib7DR1CyUKSIeRW6Njiht412udscmkKu1HUB2vee9Ynz5WIJ7RLu/ivQfBVImAKMt+80Vp92DhgVR7kC3WwA1s2iZA2gwCNTUyLKx4UvyYYHIHrR7qdov8gxULv50fMzKNjKKdUDJNp0eTs9vkgE/OVH4OLXZJnRfzbtnDqiLkfocQ6Q2FHKLBKi46OrlkREDC6E8AxdtLOl8BSq2DMY/6LzZX8g/BHjpVjknCQZzDbc/P4JbFia5UQvB4BRcf449tRYmCxWyFyIUN45LAp3DosSXOYYI0Ui7rLCOufpSGFdJ25JC0GPwYyFX5xlfXEL665Ao5Lh1oHOk9O356ZhxeY8XKnvQlqYBq/P5nQZtEYLTA5MgXad86QbIEo5X3Bus4M95p7LDejUmfDguH44Vd6CmrZeiEXAw+PiEKShOeVPD47Azvx6mKw2TB8QzNLpJWIRXpmVir9PT4ZEJHIp4imTiAWaIaVNPdiZXw9/DwVmZYRCJZfAZrPh/g0XXL7+GwafPlTjmZ2ABhc2bA0FJNSz/wWw0bziGO2Sj32WFMoZS56aM8D2pSS+6YjwwcD098n/12Yj6mOE3crKaqUYyKwMVZJAGAYvJZ9wvhVZ9WnOJu3SZhIT8osj6njSdGqgMvvpMjW30z3sfoq57VXUSJDK6Zyw8zGg9BCJFk1dxVHrXdFFxz5LAp6XNlHMz7qXPNoBop46NkxdwagFti+nGM487qGXgenvOWuIVB4HOmtdi4oySPgF4bo/AP5oMbMvhru7QupkkQgAM9JDMbl/EDp7TQKhSICEMjMivJBbTQMBkQhYMDQC5S1aJxvIogY639lsNmw8V43jJS1ICvbEPdnRUMulTo/96qxUPPJtHvJqOpAY5IFV89KgNZgFLEdAGHv7Wtlk8N6Ba4Lr3jtYgjuyIvD23DQ890MhLla3Y2CEN16Z5ULACzQoup1nX9ytN+H73Dp06kyYkR6KCF81Klq0Ak0iqw346lQVvlk8BPtXjMaJay3wVMnYIhwAvN3keHh8HFzBSy1nvdABotU7HttAza/rCvzHUHgBBte77wT7+bfpClucs2jIpzVCR02hS5upQL+0kQTVAPqOH3iBcrrgAZwnOAC0lAJrJnG2lPnfAg9eIM0fPrKfoOGOzcrpDsWOp33vkj30e2B/em5X0HfAKe4xebNECizeQwOi3g6KpZo+2E+6NuDLyVzOGDuei9nrZnL5duVxu9/1YuCnh7j1zI5qEoNbso/iIx/GHspHzS5sfQ3dZBUXOsjZG52BK3G63wA3pECfOHEiTp48ifnz5xMN+9VXsXbtWkRERGD8+PF4/PHHsXQpdbInT57MBteKigqMGOHs4fhbwGHzFgBgtJIF2Dv7OE/HXpMF7+wrwfD7qEA/VdqCr09XQiYR455RMUgL90I6zwpIb7Jg2bocVght07ka+DmocfIT1hAvFV6d5TB2BrCzQEg/P3S1CXqTBUqZRLAjfrCoEafLWpEapsH0ASH4xy0pSAr2RF5NB7KifQRelzvz6/HpsTJYbTbcMyoGM9JD8fbcNGBumuC5+MrHAOU5jMhdlJ8bjjw5Bvm1HQjxUiHYHrR25tdjZ8F1hHmrsSw7Bn7uCqzaf5XdrTJZbHhtdzFmDQyDRiWkTefXduCFny6jvJmUkl+yqz0X1nVi/mdn2J2iXQX1OPzEGLgrpOjSCxdiXYjX/3YIHwIs2Q8ceZ2SsQkvUAK28Sx56gJkU+EVQbvoB17kkjepkjxplZ5kSXadNyGMGUP/1pwH9j5LASNlFtk38L3Qbbb/x951RkdVaN09LTNpk957L4QUEnqA0DsoPJoogiAqIBaeiF2x4Ht27IhIERBUkN57LwECCSmk90J6z7Tvx5nb5k7A7wkWdK+Vpbm5M5k7ZM49ZZ+9SXVTJqfJ8Cw+OdgIjxgzx2K5/y+9whXnDDL3iAt0Uw9IGICKa1SgN5aTAqfKjqZPCt4NywwVXmDNo1JTcstAp6EEtOI67ZOGDKGi3/STydwcggYKrTQ8Yml3MnoSTd2YHUkrZ9pxsrCmKdvOZ+jfws6X3qNuMziRkD8p/ozxsquXGoczxf706WUNWHUyD/8eHoaimha8ui0V10rq0SvQCUvHR8HR2oKNdblVTfjyaA6qmzvwr3hvgSo5g7n9A3EyuwqpJQ2QSSWYnxSEACPjqFegk2j3mxFju5BfwxbnDA6lV7IFuk5Pop4WcinC3G2x+6l+aNfqRDuPXvaWSAx2Fkx6phqbpgaDAZsuFOFoZhVC3W0xt3+gQJ3YwcoCAJfYWilkUCqk8LGywpFFSUgproOHnSWrKgyQANNUXjIKUEKqlMtgIZeKXt+qk3n44UIh7C0t8MzQUPb6rxTVYfLXZ9g4/WNyEbY80Qfn82pwyMy/212FcxDg1R0ouSA83nch/TdwIHD5e+64zIL2u8tTIborVxmTr8IzwuNlKaQ9wYij6TRcDIp/WOgXzqD8KlecM0j7hQp0/usBaJLeWAGkbwd2/5s7nn8KmL2PhIsurKTfGz9TaF1p605fDPa9SDobAFBwCvjxYWD+OfIQNoVnLBXl930ODH+LBI+Y/Uz+fYCPrP2UdDdXAbEPUAOytoArztn3zBjbbUwKRLmKmplRE4Hj7wutLwFSzw9MEr/WPxH+bDFTZqahxofBYMDOq2VIKapDr0AnDIl0g1Iug6st93mva+mAnaUCEokEax/pgR/OF6GkrhVjoj2Q4O+INo0OTtYWqG7mioj+xobd8kPZ+OggMc72ppUjtbQe38wQN8X9nKzxy/y+olgY422HFJ5ob59gbkDV3d8RD/byxfpzhaTDGOjI2kwCQLtGmJsx+aS7nQorHzbTmOdBpzdAKgG7Utmh1eNfX55BZgXd3786loNf5vc161ZkYXzPVQoZhkQK/8aZ3JmPhjYNdqaUQW8wYAyvOVpU0wK9GR0NH3txY+KOwS1CHOP4YHSAvOLFg4fAJMqdpHLhkIZxCyoz1xC9KhQMBmi/XMtbzWirp/jHsIz0OgAS0ksyjUFSKfDAJipYNa0k9imV0fenPzMK1T1GTB73aMrfyq5wj+/2EPf/FtbC4r69icTosvYRy2rkf0mf5NIa4UAn5xCQc5jeC9NhWNpWmnrX5gmPM/cXj1gSa2avR0EieZb2gEMA9ziZBdm7AcD0n0ijpCaX7gvM+qlU3rnA8W/EXSnQpVIpli5dKjgWFMTd0EaPHo3Ro0eLHvfaa6+Jjt0puNhaoLJR2B3p6m2Hdo0eLSYBptYYANNK6zFj1Xm2YDyUXolDiwYIEq4bFU1scc7grFHcLaO8Ac9uSsH1sgZ087XHJ1PjRBMjBt4OlgKlYFdbJRuAGKw4nsN6YQJASlE9Xh0biQd7+eHBXkLKVmpJPRZsvMQmt09vugJ/J2vEmPEZntHbD6dybrLnJoW5CGj7CplUIEy35VIxnt3MBYHjWVXY81Q/lNQJhU/atXrUNHcICnStTo/H1iWzhfxPycVQqxR4dWwktqeUCgQ/6ls1OJRegZl9/PHOHuGkdWCYuCt9x6DXk41F3nFKAr0TiCL0+Akqch0DgYhxdK61E4ljnP+GptbxM6nbBgCTvqOOXdlVEmgb+R9KMjdO4ZTMz30JWDlxqu8pP5CKe2st2QON+UhYvDNwjSDLnCPvkHBQ7DQukABkUyZVCLuv5qiXgUnUcWUgUwK+fUit8ptBXPJ27itgziEuUHedRAqeTGB0DBJ6bppi23zOZ/jcl3RdCY+Q1cWZz+i4rSd3Yxr+DomY5Bwmm4wRRpsRB39g7lHg8lq6vviH6d8AoCZC2CjqHu97iRQ7Sy4Cs/YIPT//ZPgzxsu37uuKPv8x9RQlMEJnT/1wGZeMU56dV8tgMACfTyfue2uHDlNWnGWF4w5nVGLljARRIuVobYGdT/ZDZnkjHKwVgolPqJstPp4Si08PZ6Ndq8PMPgEYaBTkDHS2FhXvQUaF4+/PFuD9/ZlobtdiQpw33ro/CgoZV/waDAaUN7TB2UYJhUyKrx6Kx5rT+cipasKwSDeMiKJGwlfHcvGfvRRv96aV40pRHdY+wonLLRoaikfWXECbRg+JhBhKTFIoN4mZRTUtSCmuQ5yvA+vJ3qbR4dnNV7AntRw2SjkWjwjHQ7w4viOlFEt5NpSzVp/HyecHwdlGiXVnCgROIZcL65BcUIvaFjMTAAADw8xtid9BaEx2cLs/SmrkAAkX1RUAyWuo2TfoZWINWdjQ93zFcIal49NTaPno3pWK8/JrwNbHyafcpxcwYQXnm2sKtZc4Btobz7UyURuWq0hQKOUH4fGis0BNHsUPU7udzpBvYt1VlUFil/6JQNILwKnl1OjtPocavAz4Csgpm4D9LxvvA5OBMR/TfaD5JrD5IU7B/tQnRkHQWXS9DTzNkcAk+u/AF8lCqK2OYurAl6gJoFIDc48A258C8nkTpbpC+mCZYdH8WfBni5kqhfkCffe1Mozq6oG3d6VjpdGWduXJPCwZGY7HB9Drza5sxPz1l5FZ0Qh/JyssnxaHaG97PNpfyExRKWT4blZ3LNudwRbujHXulstCSvzB9ArUt2qglEvx8cEbOJtbjWhvOywaGgY7K4WoEfjptG54ZVsqUkvq0TvICUtNnCzeuq8r5iUFo6VDJ1KSn9nXH58e5hphM/v43/b90ukNeGNHGn64UARrCxkWDQvDg738cDK7ii3OAWI4bThfiNfGdsHEbt74+RJdp1IuxWMDxMyd83k1eO6nFBRUt6BngCM+nRYHV7UKDW0ajP304C2rPwAAIABJREFUJGuf/PmRbOxa2A+O1hZ4bF0yNOL6HA7W5j3r7wi6zRAW6I5BxHYpZpwtjMJ9Sltg2g/UkGsoowFFj7lUDPdfDBxdBsBATbgBRkZQYJJRxdwIqVwsyAmQa0Vnxw6+QTmfVA70e1YsHMzAi2ebV1cErB7DCVlm7aVczb0r2UOeX2EcSk2gAU1nOLSUYwA0VwI/PAAsvMzlzHy0VFMDwLRZYe9H8TJoMMfEAmjqD9DwqPI6NSQsHSnfZBocsw8Q7b+1lpw3mGGYlSPnnrTvJS5vdQ4V2iLfQfyqAr2pqQnFxcXw9fWFldVd7CrdRZhOiQEgKdQVdlYKDI90F3ghTkqgCcre1HJBwdiq0eFgegVm8GiagS7WsFXK0ciz+GH2A5niHAAuFdbhhS3X8P0c8glPLqhFelkD+gY7I8DZGq+MicSjay+iprkD1hYyLB0fJaK0rz6VL/h+/bkCvDAq3Cyd9PiNKkHyajAAx7KqzBbow7q446fH+2B/Wjl8naww0TiFamzTILWkAWHutgJK6xYTn92M8kZcL2vA2GgPgWVFlJeanYYxKKhpESmYMg0NFxsxdd7FVmnWY9TP+S7+HV5ey9nb6DUUHP360GTdrYv4fMdAUo40hYM/R8FhUJwsDjS5R6lAry8BfpnHTemvfE+FeJ8FtFeUsZP2ycNG0QSn1+OU5Om1YtsdG1dg5LvA/lfJUsO7O9Dv3xAhZgo956W11D0c9AoFqn3LhZOVsiukbMkoA1vaA4+dANJ3UCIXPoYURvNOkL1PSzUQ9yA1NlprqeHBx/lvqEAf/jbdrBpKqDHAXIfSlm5Y5uAcTKyDzrD7Oe49bCwDvp9AAf53xF89Zno6WOHQogF49ZdUnMurEcTBMdEeaNPo2OKcwakcbnJ7Nq9apOq+42ophkS6obFNw9K8x3T1gIO1BcLcOfudmuYO6A0GONuQewafisjAz8kaz48Ix0cHstCu1aN3oBPm9AtAblUTXtmWysa+TReLEOWlZqn1OVVNeGxdMrIrm+Biq8RHk2ORGOIssjgCqBHJx/GsKtxsamd3RfsEO+PU84NwPq8GYe62CHSxET0HAGy9XIx//3gVOr0BcqkEH02JxdgYT3x3Kh+7jcypxjYtXtuWiqRQF7aJa2oz2abR41xuDUZHe0BuZk9TLpOif6gL3NUqlDdwMXZWX3+8NtZM3LpTuJkNVKYKj11eB4x+n/u+3yKuYGegUlPiduQd2rWMfYASIgAY8yHZ++SfpGnHfcY98y2PcVZARWeJMfPQFvq+8BwV7oFJFB9tXIj9dPANiuOOQcCAxXRu0otAySWO5j3geYo5ptNmmQXFus6QuoX2Mw0GajbGTgO84sgWk4G9HzVhASBpCZD4LABD5w4WDaWk6s4knFfWG+8DTwLFF8WWc/knyU5o2kZKHKtzyDaJSdi9ugHPpNL74xxM9yUGjoFUFPCha6f4/Guo9XcIf/V4aW4lCKDcZlikG9adFe5wrz6VzxboL25NZYvS/OoW/PvHFOx/ZoDouQDKLTfO7SU67mKjZItPgKj1lgoZXt2Wih8u0CrOlaI6lNS24tuZ3UWP93Wywhpe85FBUU0LXGyVUClkgsEUH4uGhSHSQ43kglok+DtiRJS74OdtGh1Siurg62TFMjB/Si7CWuNKZ4dWj1e2paJXoBNkUnEuy+S370+KxtgYDxTVtGBguCu8HYR/Jzq9AU/9cJnNL8/l1eDNXen4dFocdl0tE7w/ZfVt2HKpGONiPNkc3RQO1rdxmPktuHFQ+H1NHrFpmNUWPvz7AnMOio8nPU/Nu7oCalYyuVPEWGDY21SkW9hQzOF/5hnETKW8r8JIlfftY/QK30VuGAwOvk5NA99eNFXPPki08bBRRhakEVl7ueIcoPh1fRsV6FaO9Dr4aK4G9r8EFJ2jhuywt2nYkndMeF5tHl1j9BTg3Nc0nQeIPRk6nBqbg1+lwl6vBZyCuTg/YQWtUZUkU5NiiLFBZ2EFTF5DjWWZhVAV38aFe7w5lKdyxTlAhf72hcCkVZ0/5n/EbQv0vXv34quvvoJOp8OIESMgkUgwb9682z3sTwdzBXpDmwYO1hb4eGosVp/OR0ZZA/qHurA0SQ8zOyieJseslXJ8Mi0Wr/yShrL6VgyJcMNTQ0Kg0elFH3ymeP3oQBY+OUR7OzKpBF9M74bhXdxxeskgZJY3UtGvEnfvTCk7FjIpe2M4klHJelLG+zkgzMRvEqCJVGeI93NAvB/XwT+dfRNz1yWjqV0LpVyKDyfHYnQ0TZZMd9ClEsDJWonZiQGwkEtx4HoFApytsWCQOOn1drCEo7WFgHUQ40P2clN6+GDL5RKkG9+3IRGu6B/igkfXincq91+vxAujIju9nt8EPrWawcXvOIuH3wLnYBK40PB2vpgOXVkKV1gyYHZeNs8AMo1CJofeBB7eTgFTJhfTjxh0nwNETyXKt72RklZ6haj7zVWUTHafQztG/U2Kd353hz1m8hlS2tBzMGipATZM5oL0wdeI+hk+hoIgP7FU8G6uLmHmp/v/C8y9hzV55s+9S7gXYqZWp8crv6TidA41kxytFIjyssOU7r4YHEFFTLCrDbJ5tpNRPJtI0zgJ0HpPU7sW4z47xQqlfXb4BnYt7AdnGyUMBgNe2ZaKjeeLoDcYcH+sF/77r2iz+9gA8PiAIDzQ0xdNbVo2eTyaWSX6073Ko26+seM6+5qrGtux+KcUnHh+kFlhIhdbpcBW09pCBmsTqx8nGyVG8qj7+TebkXuzCd39HdkY/u6eDFZHQ6s34D97MzDWTGKoN1CzkynQw9zFBT9zbGZff+y8WormDvpbTwx2Zlevfp7XB18fzcHB9Aq0anSobGhHU7tWQM+/oyg6Jz5mzrfcHLzigQd/Fh+38wYe3iE8pmnjinMGTHw89CZwwtgQkMqByevIRq3Pk5TYNZQaJy3GvyW/3sDT14iC7hLOTUCSltBkq7UGgIS+t3Sg/cjUn4jiHvUvSuLKU0mAiImLvzxubNa+S7Ew/wRNV8Z/wf1eQMiIKjxHu+LNVZQw932aYpipvghznR5mpkVeRsVGjxgSfzMHpW3nUytzVmvFF3+3Av1eiJcyqQQyCWDqBhvtbQ+pRAILkx1n/sQ9vVQYB7IqmqDV6SGXSXE6+ybO5FYjyssOwyLdWCq4KZ4bHobZay6iqV0LmVSCJSPDYSGXCgZPAK1Odmj1sJBLkVHegGPG9Z2kUBfBcxfVtGD2mgvIqmiCWiXHuxOj2RWlIxmVWHE8l12dHBLphpFdPQRxkLuWRkxfeQ5Vje2QSSV4YWQ45vQLxJUi4TqGwUCrj+NjvRDjY8/myvZWCkw30uklEonI0piPqsZ20fDnmlFJXm8mnzEYiMHlbKMUsFcZhLt3ni//ZjC5HAs9kLGb2xEvTqY1Pr/EzvM7gJg95tiBfRbQ162gUtOEO/84MY38E2nYYm7PuiSZ8s2N0zhmk+plmjYzuZta3EjvdJ8cALYv4GjmNbnEpJq2keJYFccUhrULPbdMQfvqyWuIFt9jLsc66vsUEDNNHOetnWl9qDMozDedBGi+SStQak+yiDtjxuaYT5e/g7j14gyA1atXY/PmzbC3t8e8efNw8KCZTs5fAAYzsmJKObfD8viAIHw8NU4gMjShmxd6BXI0kJFR7hgYLg4Qg8LdcGrJIGS+NRIrZiRArVJAIZMizlfYee8Z6IjWDh2+Ps4pp+r0Bnx6+Ab7OmJ87M0W5wCwcHCIgHW2YFAwZFIJlu1Jx6zVF/DBgSxM/PI0Nl0oxKBwV8zs4w+5VAK5VILpPX0xzIReWlbfirVn8okpoBMWX2/tSkeTkRXQrtVj6c40GIxBbsGgYLjyivQnkoLgbqeCRCLBjN7+WDe7J5aOj4KrrQp1LR1YcTwH7+3LQHZlI5RyGT6dFsd6CyeFuWDx8HAAgFqlwK4nE/HD3F7YvqAvVj7cHVKpBFZK8f6RuQnSHUNTpfjY9W1iGuf/ApUd+YHbuAGQACHDuW6ddwJRzPnw70t7M/yArmsnMSUGBgP5Vx55BygwEbJT2nDFeXsjiWlk7SHq965FwFWTyTaD+JmABe8G5RZFQiOaVqJfnv+GaJt8FJwWdlAB8ilW2gB9FnLHpIpbdyj/v8g+RH7xe5Z0Inz0+/lWAvdGzNx8sYgtzgGgpkWD7gGObJMOAD6cHINAF6NDhZcd3rqPE0oJc7fFI30D2HgV6maD2YkB2HOtTOCJXtHQjp+NrhhHs6rw/dlC6PQGGAzAlssl2J1KCWZhdQve2Z2O17enIYtHgVSrFILJToKfAxQyYWxg9rYBIIvn3w4ApfVtaDLusp/Jqcaqk3nINJ6zaFgY65krlQCLR4TD0kKG6qZ2rDyRi5UncgWJ3ZdHczDwg6N4ZPVF9H33MJtk1rcKRX6Y7xODnQTHLRUyJPCapDN6+xuTcvrZ8yPCWQX5CA81Di4agNfHRuKL6d3w3SxuKuZlb4njN6pQWt+G2hYNdl0rw9Svb7Hv+Fth627++I8zaef7TkGhIl0PPvwTaWfx9KfcMb1W6I1u42rc9TZJd6wcadrEpyd6RNO0+cGfgYWXaOrf3gisSKJ4uXcJ8GUf0ufIOSxuWuYcokRu5k7g5SrydffpTs2Fqz9S3Gw0iha11QPr/0WFfFUGTaouf0/ewab3AT/j3rTaExj/udEnXk5Jac/H/3/vY3sjieQdeI3Wr6RmCgBzlNi7hHshXhoMZMXKh1wqwYQ4L0ilEjzFEyuTSCAQL+vL2/cGgN6BTpDLpFhzOh8PrDyHTw9n47F1yXh3D1e0VDa2Yd3ZAuy6WoYOrR49A51waskgfDerO04sHojpPWmVw89JyGD0sreEQibB3tQyjPrkBJbtycCs7y7gjR3XBee9uycDWRV0L21o02LJz1fRptEhq6IRc9ZexJncapzLq8HcdReRVioW82Twwf5Mlkml0xvw332ZqG/RCPJqgBocCX6OkEkl2PxYL3wyNRZvju+CA88MEF0Dg7O51Xhndzp+OF+IDq0ebmolAk0Ym0zsH9PVk10tAqj5el+cF+QyKd6fFC26ZwAQsJDuOPQ6M8c09If0w3Rg5SBg7Xjgy940aQaokXbyY2DTQzRJ5j9HQym5ZaT+bF7orDPI5JTXBfTjVlqYWMOHXx8q0vlrR231pIbOIHQ4xVMG/v2oOWow0FT+yDvC9Z8bB4S/g/l+6FJqTACkJTRxJac34hUPjFtOjFXTxkRncZ6PmjxaHdrzPAnt8Y+vnwx8EEEsVmbtqjwVWB5HbK6144Etj4pF+wDcrRzzti11mUwGCwsLSCQSSCQSWFreRWXDuwitaWsTtKftfgulRpVChh/m9kZaaT0sZFKEGCfQe66V4btT+VDIJXhiQDASQyjAmlLNP5kShxe2XsXVonr0DHTEO/d3hc5gEKkGm07327U6rDqZj8uFtegR4EiFtkyK++K80MVTjbN5NYj2skOMjz3aNDoR9f3rY7mY0t0Xr4/rgmeHhcJggEioLa20HpO/OsNOYAaGueC7WdyEuMIkON1s6oBGZ4CFXIIgFxscXzwQF/Jr4GVv2Sm1s0Orx4QvT7MKod+ezMPWeX3RN9gZxxcPZDu5fEilEvQKFCautmYmP+aK9jsGS3uiffOhaQaub6cOp7ad6OZt9bSLbu1s/nk6Q+R4mipr24TWPjauRLs5+Lpxwj0diH9ErHQOCBPDfS/SPjhAlkJjl5sXTSo4Q3RzPjJ30V6TKVxCgXmnKeCr7GjnHABWDedESI4uAx49wu2AukZAJPrmZmQ5DHoJCBlGE7CAAXduJzz3KPD9RO53pm4xc9Lvu0t5L8TMTReKRMe2XirBk4O4xDLa2x6HFyV1Op19dWwkZvX1R3VzB6K97CCVSswSMxj2fE6luLmSXdmE6qZ2jP/8JKuuvulCEfY81c+svaWnvSU+e6AbPtififpWDab18MX9cVxnf2C4Czae564tztcedlYK/HdvBr44So1TqYR2MkdHe+D0kkG4VFiHYFcbeNlbora5A6OXn2STtxXHc7HnqX5QyKX46GAWe30NbVp8dDALq2f1wJQEH9ahA+BE6CYn+KCioR0/JhfByVqJ54aHwYG3SqRSyLBiRgJqmjugUkhhZTK997CzxMy+4s+RVqdHfrWwUZZaap7GeUfg2gmTKW0rfWlXUNxsb6LkKOcQpythjnp5K0xcSRZCpVcoARxjtP8xTZo6m+Azr8nel5qGporFAMXkYN60OX2HUGyouZKU0c0JdfJXoJhJuV4HrB5NTVGAVoAePUxUdFPXiewDJKA0ea1QCC7hEe6cmKmU+Oq15sU6GVzZQMwvlR1ZLfn2pGR5zThOvPTMZ7SD2WzSlDal+t9F3Avxsk2jE8U2A8CuKc7pF4geAY5IKa5HzwBHAZvxnQldoVRIcS63BjE+dnhjHDU6V54UCl+tPp2PRcPCUFTbgvs/P8WKZPYIcMSmub1gZ6lgNToYvD42Eo+uTcbNpnaoVXK8dV8UJBIJvjyWK1Ce//5sAZ4ZGsrmidkmsbihTYuqxnYczqgUuOroDcDh9Ep08bSDTm9AckEtHK0VbCOxvEE4me7Q6lHT0oFxMZ7IrWrG+nOFsFXJsWhYKKsWr5TLzK418bHtSgme+oETHTt+owpfTI/HVw/F45VfUpFV0YikMFe8MIqsr+ysFNj5ZCK2Xi6B3mDA+FgvlgmaFOYKfycr3KgUKtmnlzXifhOr5TsGa1egUbgqCrdoatZl8FgwN7PIaWfAc8COp0hoF6Dd6bpCWhGszCDXISaW+CVSg1AiAWrzSbSttYbyyeDBt39twYOJbn7mcyrg+/2bHCpMtTUAYZyVysjGrDKD4rG7UQybv6997D+kp5Ewi/JFvrORq9GmzNYdmLWL7hcKq1sX3Oag0xDdPX07Cb4Ne4tU1ptvkq4SI+h8aR3pSjkFEUOVeS1X1tN7N/5zEoXjx+hrPwKT13POR+x7ZuL6dIdw2wI9Pj4eixYtQkVFBV599VV07SpWIP8rwFx/Y87aZPQJcsJ3s7pDKZehqrEdnxzKwo2KJgyOcMWcxEBIpRJ04VE3kwtqMW8DJ752Pq8GB5+lLp/BYMD5vBroDAb0CnCCr5MV1s8R7wv9q5u3wKLnYRNRjRe3pLJiGPuvV6CkrpXdIQxxs2UbBUAnOi68Y+pOpvGrTuazxTkAHMmsQmpJPaK86FrHxdKOJIORUe6CYlqlkAksf8zhxI0qgX1Hm0aPH84X4o3xdANinq+2uQM/JhehqV2H++O82L31svpWvLYtDUczxRPtZhMF5zuK6KliCwuAinS9joQwmL29Q29SssUUnFn7Kfnx6yveJ9LruF0Xqcy8727YSKGdGUCBK3gI7f4ANIFmpiaaVqEgCEDB0FyB7hQEUQHtxJsetTVQ8mzjRh1Te1+hOEjmHqFCaEs1kLya2+txCiKxjSNvk+1P+GigB0813qc7fd0O7U20O+4ULPwDL06mhoJDACWocgua5vOvp7lC/HxWTuJjdxH3Qsx0thELE9pbKXC9tAG7r5XBw16FCXHesLSQ3ZI67eNoJRDGHNHVHcsP30BxbSv7eyZ2o2QsKcwFy3h0cImEfG33ppULrM9aNTr8cqUETw8JxZHMSrYYn9rdF/MHBmN4F3cM72J+qvvy6EgoZFKcvHETEZ5qvDQqAq0dOlbACaCk8/Mj2Rgd7QFblQIDQrk4t+NqqWCyUtnYjm1XSjE8yl0g2gYA1U00yXh1bBeEuNniUmEtuvs7YopR40QikWDh4JBOrYAYmFra5VQ1Ia+qGT0CHQXxPbmgBvuvV8DX4XcucNQeQMR4IH2b+Z+nbKACff/LJL4DUHLZUMxZp9UVCpuBSh57p7WOYo1TEH2ZUt8Bitl8scseZujZKZuArbzj2YeBJ07972JowYOB3gtomgUDaWmEjxWfl3uUK84B0va4uIpeo0QqbLYyzY6wEfRlCuYeIpFwxXljOWl/uEVyDYKsfTT1YVBwGnj6KjUF+M4ieq14Wg/8rqKa90K8tLKQiSjuNibK49He9qw+ER+O1hb4ZKq4EpSbFCZyqQQSCbD2dL7AweJ8Xg3O5dWIBhsAEOfrgNNLBiGnqgn+TtasGrrBpJtgMDk2OMJVINYW5mYLH0crkaYQAAS4WKOqsR1TVpxh871pPXyxbEJXjI/xFOgSxXjbsc/xzNBQPDM0VPR8fLRrdfj8SA7O5lYjxtsOCweHwFalwJrT+YLzdl8rR0VDG0LdbLHpsd5mn8vB2qJTu99QN1tRgZ4Ueuv89jfBOx5INynQ5Qqgpkp8bnMVaRCZ6vikbKQC/fzXwiKy4CRQeJYaiKtGUC4F0PBixjYSLAaAqizK5/x6i6no5ijyvr2JPs4UslIFiVOawjWc+39NG7GG+Dj9KRXo45YDmx+m/XJ7P7HmkJI3+GusoKKf/zqrc8gWuCKNxOBGLCPa/okPqbAGqEGxYTLw1FUq2JniHKB8/tpPpN9haoGcYxTI7RD+TQAA7L2A3k8aWax6wDWKdt3vAm5boD/66KO4fPkyIiIiEBgYiEGDBt2VF3K34WJrgapGMfXjdE419lwrx31xXpiz5gJrNXEurwbtGj2eNEmeDqVXCDqlGp0BRzOrMK2HJR789hzO59EfQFcvO2x6rJdo6gFQx7RHgCPSyxrQL9RFkADq9AZsTxF+cLdcKulU5Ecpl2FW3wB8dYyjzT/eP0hwjl5vQHJhLSxkUlYkTqMT7+Tzj704KgJuapVR/dMeTwzgnrO1QweZVCIo2LU6PfZfr0BxbQuGRrojwNlapBQKiPfo2zQ63P/FKXbis/JELrYvSESwqw2e2XQFZ3PN+IwDcFHfRQGPno+TUMWN/dwxa1dKQnOPCkV1Wmso2Rr2pnAPEiB7iJ6P0cR95zPA1c1ULA5/m5SNAaDoPBW0zdVA3HSgFy+p4mPqRvIZrysiGhE/CEpMOowS43us7eCuIWQoJbeDXqYupq6DREF6z6efV+fQdJzxNo+aCPzLRPTCdC/S3LHe8yj4alqJQtoZzn9DgVoqI9Ekxnbj6mZ6rzqaqHkwfTPtdWbsBjZN55LZzN20r2SOvRA0iOinDIa91fnruAu4F2JmsKsNDmUIkwVrCznGfXaSFYzbdqUUm43J0Knsm1h+6AaaO7R4qJcfpnTnbHgMBgMa27VQqxRQq2iS8cvlEmh0BoyP82SV24NdbbHioXh8fSwXWr0esxMDEetjj+Jak7UJECOosqENj61LZgvj9/ZlwsveEvfFCacvRTUtyL3ZjAQ/B1gr5Vg6XuhZ2tyuhd7EyNhcfATMi0HJpBJ42Vuid6ATzuRyawGM5aVMKjHrtHErdGj1OGJsTCaFubCxdPmhG/jwANkpqVVyfD+nJ6K97bE3tRxPrE82y1AAADMMzjuLSaupeVZyiUTT+E0za+NUL/uQ8DFlKTTVaK2lyQaTZJ5fCTx2jArQM5+TyJuunejt0380/5kf9yk1RCvSqHAOMvOZY6ZPDCrT6DV4xgLH3qPmgVJNbB8+VTNiHFHmGccKGzeaagMUy5OWUFxS2XHXWX6VLIa84juhs2qJeTTiXZr2dDTRFIaJx6bI3APsXkximpHjgHGfUQKbcwTYMIXeH4DEkvotIkopH5pmuneZUxv2iqWfM8lrzAOcE8nvgHshXuoNYi/0lg4d+r57GCqFFE8PCcXYGE8A5nMlc5iXFITnfuIKh7n9g6CQSaExY7rOZ4g2tGlgq5SzO+UWcikiPNSC82cnBuDpTVfYeDEp3pu1HQOAp4fQv//hjEoEudpgyQjKN4ZGuOFf8Zya+vgYT4yM8sB7+zIFw5iN5wvxYC9fPJIYACsLGfZfr0CgszWeSBLmprfD0h3Xsf4cCS6ez6tBYU0Lvn4oQZRHyqQSs2LJAK0uVTS0ISnMhb3Glg4t/rs3Eyezb6KLpxrl9WLGTXGd+L5zx9BrHjXRmM9tyHAaxKi9aH2FycMkMhpGSGWUOzbxBhBMXDWbl2koh2WKcwCAgZx0AgcQHX6P0TlIqiDGTvioW79mqRSYuYvWcJoqKIdlpuTFF+l1+PQUNjwlEnF+ygypPOOAhUY7YBt385Nyg4EYU5fWUoyNGAtMXEXDmU0PkkAbQILKUindB3JM7jMNJUBVOhef+VDZASp7otLzhT2Z60p4hHJNJu/0SqDX7RlH94nb5bm/Ebct0OfOnYuNGzeif//+d+1F/B74ZGocZnx7DlozeVdFQxuKa1sEPpAAsOtamahAN9tBdLbG3rRytjgHgGsl9dh6uYTdBeJDJpVgYry36DjzM0drC1TwqEH8fW+93oDtKaVGQTgnDAp3w5KR4egV6Ii00gb0CXJCnC+3x9jUrsUD35xlhZKSwlzw7cPd8XAfP+xJLWPp9vF+DgJ/d4VMiscHBLFKowA1D17+5Rp+vFgMpVyK+YOCMS+JhOAWbLjMCpK8vy8L62b3QJ8gJ/QIcGTfF2cbC1GSeiSjUkDHbOnQYfPFIiweHtZpcQ4AE+JuIT7xWyGTUyJYehm49jMpPnYz2nnpzOyf6DSUhJ01EY84/SkV6Oe+ItoMQMFo6+O04ydXAesmcJ7ee5eQMiVDOde0UXBxCKCAxCgc86GwpKL+JOPDKKGpd0cz8O0wUjUGAJcIYM4BEoNLeITo+fwpyZnPuZsCQNOsxGe4QAUQRd05lGhXACWz3cxM6hWWnPiGppWoUXbeXFMh74TQa3j7Apr8uITTniezR159g5LzyWvoPeRPmjJ3095Qr3nUGa3Np+MxDxA1KX077RiFDKXd/t8R90LMLKoVJyzXy+oFau7n82qQWlIPJxsLzFp9gS2Un//5GtzUKiSFueJyYS2e2XQF+dUtiPJS47Np3eDvbG2Wmg0AgyPcWBE6BkMj3dDN155VjQ9xtcHEeG8cz6oSTa1PZt/9aNDyAAAgAElEQVQUFOjfHM/Fsj3p0BvXfNbN7iGaYlkr5ZjS3YdNBAFKYBkUVrfAycYC1ko5xsZ44uvjOSiqIQaAl70lxsdS4v31jHh8eyIPOVVNGBLhJmoU8HE0sxInb9xEpKca42O9BCJ1LR1aTPjiNDKMu/ChbjbYMq8vtDo9PuPZGTW0afHJwRv4dmZ3rDmd32lxDgCWZhrFdxRSKSVPTGHLqABbu3IClG5dhEmQ2otEfk5+JJwAVaZRkesRTZaTjOhj6SWajjCWi3VFVJD79KAkiSmaO4MpbVsipWT46o/AEaaJV0K78wuvAPbEdIDShsSUmP3Orv8SNgn40/6DbwgVkMd/QUrLrl04gTuFNWl8AHR/iHuItDs6W5VqawB+ms0Ji6ZtpdWAIa+T4KeORyM+9h41mM0V4k7BNFULG8WJGintSNHezovec7UXUeF/R9wL8bK5XSNiaWr0BoElZZQXTY/nb7iEfWlUaL2/Lwvfz+mJHgHiJH9Sgg8iPNQ4axSJYybk03v6YsulYnY1MtJDjd5BTiiqacGCDZeQUlwPX0crfDg5Bgn+5ouH8bFe8HW0wtHMKoS522KECePIQi7F4hHhWDwiXHBcKpXg/UkxWDw8DAYAbmpqrlaa2deuaGhDF087TO3hi6k9fEU/N8X5vBocuF6OQBcbTOjmBaVcht3XygTnHLhegQ6tHvOSgnExvxYdxkbqQ738REwjgN73bVdKAVD8/+nx3ghxs8WbO6+zq07ZlU0wJ2l0LPMmJifc/nX/T/DrDcw7Q+szdt609gjQBHj2ftKIaG8kVo630cps2Fu0H63XUO7IONl0n0MxjPE094wjmnuZGecaGzeaxh/hDS30Glq7YQr0lhoaHLl14WIgA5WahjAMdBrS0cg9St97dwdmbKecGSCnit7zeYMridHFwgiplHQ1GDSU0rUxRW/OYWJpMkjfQUyCkKFccc6AeQ1uXYTCpRY2NKF3DgW8e3ADNpdwEjmWSoEJX5NDSH0huYaM/A+dEzwYeGQ/xVy1p5CZys9z7xJue9e2s7PDmjVrEBAQAKmxw5GY+PsJiNwp9AlyxpkXhmB7Sgne3ZPBFqYqhRQjozzgaG0BawuZgPZtauMAAPfFeeFIZiV2XyuHVAJM7eGLfiHOIsoNANQYKY6XCmux4lguOnR6zOjtJ1KiTCutR/7NFvQJcoKDtQVeHh2JRZtT0KHTQ6WQ4kXjHg0AvL4jjbWnWHkyDy+PjsCcfoFICnM1q3D5c3KxQMX4aGYVDmdUYmikG3Yt7IedV8vgplZiQpw3JBIJtDo9siqa4ONoKRKr23KpmA1q2g4d/rs3E/2CXWCtlAnUQjt0eqw8mYeegU5YP6cnDmdUor5Vg2GRboIuLQDRDjpA6vRymRTh7rZskspAKgEmdvMW7JXeNTCdMj6CBtEHm1GZVFhzH1rRJNsY9YtNVOj1GhLn0Wu54pxB1l4q0DP3EEWxtZYmyNN+ECqdV+fQ/qTSlhK1gAHGqc0AmghdWssV5wB1EK/9RNNtK0dx169N2Jwye0yuJLuPlE2UUHedRFOgxgrg8FKOajTgeWoo3MwGVo/iur69F9DEKf+E+Hfln6Bk3XQns9rIDJGZ3nyNFE+1B7DgIlkNWTtzDYUu99HXH4B7IWYOiXAVJUfudpaobhY2qCzkRBc3LZQPZ1QiKcwVizansA241JIGvLItFetmcwVAYXULPjiQifzqFgzv4obH+weJ7CWVchl+fLwPTmXfhEanR78QF1jIpYj0UIu80CN5k6LGNg0+OJDJTrbqWzX48ADthZvizfFR6BXoRKymEBf0DnJCZUMbHllzAaklDbCykOHVMZGY2sMXuxb2wy6j7/voaA92b1OtUogom/WtGpTWtSLMzZa9rrVn8vHqNk6N/EJ+LZZN4BphO1JKBXEvq6IJ26+Uol+IM5uQMqg2umGYi6N8/KuThvBdwZDXqFiuK6BVHyaJGbGMaO3l1wBbT+C+Lzi6tikkUmrAmToyVBsbFBe/A3Y9S007hRXFR4a6CQjjI4P+z1ES12S8V/V9mgpTvgc4QHG58IwwOVXZCffAAdqDr82jmGvlSEwpvmgSAJz6mJhRs/eRx3pbHanA85ujFlZcQluZDpz4gCj9sdOpGVCVIXT9ADilZdMYrW2j15HwCCW3OYdJBK73ArovAMCU9bQu1VhG61TMHn7UBPwRuBfipTl7MD70BuBcbjX0BgNbnAPGXOlELlugZ1c2Ir2sET0DHeFqq0KUlx27dsigi6cddi/sh+0ppXC0tsCEbt6QSSV4Y0caO2QqrGnB05uu4PhzA9m4U1bfijaNnh0yxfk6CIY5F/Jr8I1RnX1mnwBWWwmgwVB+dTM87S2hUsjgqhbauo6P88KWyxzz002tRJ8gccOptK4V7+/LRE5VEwZHuGFeUhDkMil2Xi3Fgg1cQXk4oxLfzEiAt4MValu4v3F3tQoKmQSJIc44tGgAjmVVIcjFRiAEyiCropEtzgGKxSuO5+K9STE4nCFcmzRDShCtAdxxOAWRDa0pHAOBUf8VH4+eTKycsqvEzLE2XrNHDK3qpP5MOVD0FCo6veJpYMGs/jgGUfNOrxVTt5k4knuUlNo1LTS9H/Mh10w0h4xdXGEMAMUXaEqfwKO+D36F2E1lKfT6TXNqgIZRP84kAWOpnAYvw97k8j8+qrNpWKX2ogEWA/do+u/Al2gPvvA0sQ5GvU+NBYBU4HOPUmMhaBCnE+LXB3gqBWiv55ThGfza1cy7gNsW6A4ODsjIyEBGBqcg+VcLngxcbJWYnRiIBD9HrD6dDwnIroYRp3h1bCRe2ZaGDq0eHnYqPD+CCiK93oDKxna42iqhkEnxxfR4lNW3QiaVsPTMkV098MH+LNYPXaWQYkyMJ0rrWvHAN2fZbuexrCpsndeHneK8uyeDpafbKImyODbGE70CnZBWWo8Yb3tWOKhdq8MPPIEjAFhzJh9z+gV2es2mXsQAKYACtHfz7FAugblR0YiZ311ASV0rrCxktEPEE+tIMyM0lFZaj+5mur9McFPIpJ3ugwLAgFAXxPrY44pxT8nFVokHjLYa70+KwcIfLiO3qhnh7rZ4b1I0/JysO92r/10gt6AO55WNlGx1nWTc7QZZPRx5mzuX2d/260sTXfY5VBQ82+og2gl3CacO5/aFnKBbTS4JbTz4E3mlb5hC3pUKa/I67zYDCBpIXwzazaiZt5s0A1praQouldFzpG3hptQu4bRzBFBHtaWGLOJUdmL7nc0zyJMYINaBto0K8RMfCClZZz6niZG5AO0RS0krf9IEcF3dxKeBvOPctCh2OrePJFMIr/0Pxr0QM8fHemH3tTIcTKdEpk+gI/49PBwPfnsOLcYm5vAubgh1s2W/5yPE1QYtHVrk3hQmAtd5MUSvN2Dmd+fZc1KK6iCVSFjWDj/uyqQS9DfZCQx0scFrYyLxwf4stGh0GBvtIWDoNLZpRQKcpvGwoU0DK4UMcpkUY2M8WRoqAHx08AZSS+j1tnTo8Or2NAzv4g4HawtM+xUToU0XCvHa9jS0afTwd7LC6lk94O9sjdUmzdwfLxbhlTER7DpUU7v4/Wxq18DH0Qo9AxxxjsfUmpRAn4EnkoJwJreabZRMTvCBr6MlrhTVoUeAI2Z1wli4a3AOEU9xHQOAx0+SS4aVE0d1jJ9Fgj1txl1Vj1hKngw6mvjwY0j4KJpiH3yNi1WaFuDQG0DgYZrArJ8sjo8Axa+nUozFty8Xtz3jxNaaHrG3vj6+8JFSTbaXLhEQqd0wr1FpS7uOfNQVUuORUcFvbyIxuRbjmkTOYZr+BPSjuMsvxhml5fiZwN7nueOR47nm60NbgdoCeg5rXgEjlQKhw259fb8j7oV4qbZUIMzNBpkV5lxECFFedrcUyfzmeC7e3k3K0hZyKb59OEGg81PfqoGNUg6ZVIJAFxuWhs6AiVUMimtb0dCmgb2VBV7aeg0bzhfCYAD6hThjxUMJ7D46ABRUN2P6ynNs/DiaWYUdTyYiwkONzPJGzFl7AUU1rbC3UuDDyTEYFC5kowwIdcGqmQn48WIxHK0t8Fj/IBENHQAeWX2BbT6mFNdDpzfgmaGhWHta6BN/wKi99OrYSMxdexG1LXTtS8dHsdR9H0crQbxv7dChuLYFgS42kEklrAsRH80ddCzE1VbAUjUdzgHAouG/35rHr4atu3nXDKcg8844939Ju+QtNZTPMZZtMVOJqs4gYSb99+AbnBOPQUdOD7HThXoXlg40rAHMOx7x4zWDwCT66gyX1lJxDlAD4fRyimUhQ4D9Fpz3OSTEAJLKaO/7lycojnol0LoQQE2KR/bQ+pRSLbS1lMo6F8qTSsXF+R+M2xboy5YtQ1ZWFrKzsxEQEICIiIjbPeRPjxgfe3w0RXwDntLdF8Mi3VFU24IIDzUUMilSS+oxb/0lFNa0wNuBFIJjfexFHuluahW2zOuDNWfyodMb8GAvPwQ4W2Pd2QJBkqjTG7A3tRzR3va42dSOb05wSp1N7Vp8dvgGVj7cHS62StFEXCqRQCGTgB9HVMbdxNYOHdadzUd2ZRMGhbthRBR9iMfEeGDF8Vx28mKrkmOoid0ag2V7MlhKVkuHDq/8korhXdzZQJsY7CxILmVGxXV/Z2sMDnfFIWNXUiGT4JFOEsJ9aeVYeSIXEkgwtz95aG5+rDcOplegqU2L4V3cYWdFwSDKyw6Hnh2AhjatSIX+D4XKDuhlxtpmwGLawSm9RArDDLW6x6MURFI2EOVz2JuUMFk7GWmKy6ioDRpEz9tWJ1bVZWjlR9+h5BOgqcruxUQrNQ0sUROA4//linyVHbf33lAG/Pgw0YBsPYHxn5II3aw91P20cSfalFRGlNKjyyhAesUDD/woTPaaq7ninEH6DirQm4U7zICBgmbYSJpenfuaJmV9F3LTrwc2kS9wVSYF4n6L6Lh/IrDgPInwOQb+OjXSPwj3QsyUSSVY+XB35N9shlZvQLArCbYcWjQAB69XwMPOkrWcjPWxx/yBQfjmeB46dHqM6OKOyd19oJTLEOdrj8uFnEgQ31Iou6pJVMDvSyvH4wOCkFpSj/kbLqGgugVe9pb47IE4wbSHwcy+AXigpx+0er1I78PT3hK9Ah0FqzKMjWZ9qwYLN17GsawqOFpb4LWxkSLl4NwqYbLdodWjuLZVoLQOAMW1LVh3tgCtHTpM6e6DLp52aGzT4PXt19nYn1/dgvf3Z+KzB7qJtDkUMqlgt31stAc+PXwDdUZhPLVKjnEx9Nq+eTgBq07mIe9mM4ZGumFMNDUUegU64dCzA3A0qwoBTtboG+zUqWfyHw5T5XSnIGD+ObJkU9kR80UmByAnUaMjb1Ph3XUSFaTtjUT75oOJNUdM4+NzwvioUImbeXEzqLF4ZSMJdw56mXawb2ZT4V+bR3vo/RZRTGwsF64ztTcAx98Hpq6nWM+3fOvzpPj6te0kjpS1h+Jf/ExSo887zhXnDNK2kljclPXA3heIgtnlfq752+txYhFlH6Tmpum0y8EPf3bcC/ESALYtSMRbO6/jclEdBoe7obalHZsuFEOpkOKpwSHsJHxQuCs7wVXIJHgk0R9tGh0+OpjFPleHVo8PD2ShX4gLqhrbMX/DJZzPq4GrrRLv3N8VQ8zkcH2DndndcADo4qmGvZUFzuVWC9Z3Tty4iU0XCgVrRgx1nIFWb8C+tHJEeKixdGcau9JT16LBC1uu4fQSV8FaDl2Xm6hw56OopkXEiNyXVo5nhoZCqRAyEKQSYlJ293fEmRcGI7O8EUGuNp0Kku5PK8eiH1PQ2KaFl70lVj6cgDgfe0R5qdnGhVQCtrH62thIzF2XjLybzXC2scCHk2Ow91o5frpUDKVChtfGdEGQy130Qf89wXeWYDDmY2pMll4hBhCzVtl8U3heewPFq5Ya0gAqvkCxdPQHpFMUPtqooWH8d5UpgS4TaMiU/J3RbSORGgLM/ejc1+QwYe0CDHwR8OrG5bd83MwiJtb0H8leTtdBAx5mmu2fCCxMod9tbr/8/+uu9CfEbQv0devWYefOnYiOjsaqVaswcuRIzJ49+/d4bX8IHKwtBAnYi1uvobCGOkrFta1Y8vNV7H2a25UyGAxsIhTiZou37hMqkHqbUdRlqPPN7VqBZQUg9szlQyGjve//7iXbLakE7I78/A2X2KC/+WIx3r4/CtN7+iHcXY2Nc3th/bkCKOVSzOobAFdbFZratTibUw1/ZyvWEqOgWpgsN7RpUdeigbsdJZRDIt3w8ugIrDtbAEuFDE8NDmGtjr58MB67r5WhqKYFw6PcBTYiDFJL6vHE98lsxzi5sBa7FiYi3F2NUV09ROcDpHT8pyrOb4fAAUKqJUBJ3Yh3uN1JPhKfJjpiRzMlWgBNW7y7UzBkEGacJN/MFj5e2wrUF4sLdFt32ptMXkNTnPiHuV2fA69yOzqNpbR782w64NuLvhjUFlCxzEyBSpKJsjnsTe4clZr25lt4gd2JdAkQO41sgxi4duGm50PfoEQYEFoF2ft0rojp4C+e3v8JcS/FTFMrMw87SzzU21903nPDwzG3fxA6tHrWvgYAlk+Nw2vb03CtpB59g5zw+jguWXBTq6BSSAUNTIZ++dLWaygwUuNL6lqx5Odr2PcMxd3mdi3O5lbD19EKIW62sJBLYQHzFNOvH0rAyhO5xsalKyYZFdQ/P5KNY1lU1NU0d2DxT1fRP8RFEPuHRLgJptVe9paI8BDGtfpWDe77/DTrh77pQhG2L0iEQiZBq0Y4kWHuIwsHBWPBxsts7H98gHDa5KpWYceCRGw8Xwi9AZjWwwfudsTUUqsUoskZAx9HKzz0/xCi+1PB1t1809M1gqx7+FDaUtHNZyXFTqf/VpvGxzZhfDQYxJR6mZzEhUa9T/RKqYw0RdZP5LQtyq/Rz/o9Sw0CU/9zZro99E2abpddpfsAP54yuLKBmxYZ9CQyGnkfTfVNwRwL6Ac8cVL8c4AmTcwO618Q90q8VClkeOt+Yf73ypgukEoAOU/A7CtjrlRc24JhXShXamjToM0kXtQbG3Tv7ctgdXwqG9vx7OYrOP/SENGE+rVxkTDAQA4VHmosHU+xtqBGLHZmeszcOidzjC/+BgAVDe1oav91QxN+fuxso4StUs6yTAEg0IXi/RNJQTiXV8M2Cab39GPvIyqFjBU3BoiFVdHQht5BTlApZNDq9Hjpl1Q0GpXtS+pa8dau61g/pxfWz+mFDecKUdHQhrExHoj3I3ZJiJstDi8agOLaVripVbCQS9E/1BXvTIy+7TXdE5ApaAhjithpJCLMIGIcaXDsXcLlo621xPAMGUYrQo/sJY0gvY6e0yUU2P4kx0q68j01OQe+SOuRe3iT/uILwDOp1IS8wFN7l6uAQGMjNTCp8+m7VGq+OL9HcNsCfefOnVi/fj3kcjk0Gg2mTp36lwye/ysyTTp+WUbridrmDjz3UwqpXLrYYNmErmYFOQaEuGB8rCe7C5MY7IwJRlshPydrkfLvVJ768aqTefjiaA70BgMe7ReIJ5KCMC8pGL0DnZBa2oDegY4IdrVFRUObaKdm4/lCVqAu3s8B8X5cAXe9tAEPrDzLTmieHBSMRcPCMDLKA58d4RKcWB97NjFkMKdfoFlKvYVcektRJID2ivj9CJ3egCMZVQh3V3f+oL8DVGpuR4bB5HXkh16RStPipBfpePgo4cTawb9zH2IHf84CjQ9TS4mWm1Som3oS1+aJE9GaXOH3MgUw9mMSL2lvIDVMRjU9aiIF2tQtREfvvUCYHN/Kw/cvjL9rzDSXsPk4WmHVTPP7W3aWCrw+tgve2HEdrRodQlxt8Kxxh5tv8QMAWZWNMBgMyKxoxLQVZ1nbtSeSgvC8Ucho4/lCfHLwBlo1Ojzcxx/PGn19Fw0Lgymum6zrtGv1yKlqQoI1F8NnJwagQ6fH7mtl8HGwwr+HhwkSbYBcPZjinHmeLZeLsWREuEhDg1n1GdnVA/vdbXE6pxqRHmpBbOa/b6YCTf+Ah/u/pp3qsqs0EWfEKsNGEoWdgb0fxcfaAqJDFpwiNfj7vxJqegAcbROgXXCmOGeQtZcKdOcQcsAoPM39jPn9Eol5q0w+aszsVdbkUEHfewFN5w162i3tzNXjHsK9HC/NauyYyZXUKgVGdvXArquc7sfUHtRMvF4mjFUNbVoU17awgxX+c3w4WcwMTQp1gaVCxjYMJRKI1g6HRrphVFd37L5G+gxJYS4YZ1z3GRrpxuoeAUAPf0c21l8vbcALW64ivbwR/UOc8Z+J0XCyUaJNo8MLW65h59VSuKlVeH1sFwyJdMPS+7rg5a2paO7QIcDZGouHU4zrE+SMw8ad8gBna7P76wA1bhk2gJtaic2P9Ya9pYVodSn/JjUg7CwVnSrHSyQSgQXoPwCQ9AJg60F72h7RtAsOUDzko6OJRDrdIsljfPxn3M90GmIj8ZG8hgp0pjHJoK0OKDhDKzf3fUWWwUob0gtRmx/a/Z1w2wLdYDBALqfTFAoFFIp7M6nuDElhLgJRD4Z2/u6eDHY/80ZlE+ZvuIRTzw8SJXBSqQSfTI3DM0NCodHpBR7mAFEW157JR/7NZgyLdGepSxfza7B0J6dS+J+9GYjyUqNfiItI3MPSQgYLmVQgIHSr7ubyQxx9EgC+PJqDh/v44+khIVDKpTiSWYkwd9vbelTeCk3tWvx4sQjVTR0YG+OJMHdbBLnYiM5jqLP/wARqD1KWNEXvJykApm8ndfdBr9DEx2AgUSYbt9srSwYO5ETuABIPsTczefPpKbT8AIjSZIqIsUTPryui5FUqE55v7jH3MP7uMbO5XYvalg6zUxlTTO3hi9HRHqhoaEeQizU7bUkKdRUITw4IdYFEIsGnh7MFnugrjudiZh9/1DR34IUt19jjyw/dQIS7LUZ2wsxJDHHGyWyO9eFgpRCJMUmlEswfGIz5A4M7ff3m9DDsLBWQSCRYNbM7PjqQhdybzRgW6YZHeY3NIBcbs/HwH/xKWFhx6y989H6Sdhivb6OG46BXKR7tfJqKc4BWkLY8ynmwm4OdNyC35JSRAeFO/QObaOJTk0tTptDh5p9H0wZc/YEE7yLG0tpT2Gjg9Gdg99VlSrJYA2g1qJdRHNS9q/nnvMfwd4+XDD6YFINuvg5IL2tA/1CuQE4MdhHsl3vZWyLA+dfHDle1Ct/P6Ykvj+agVaPFgz39RJ7pMqkEX0yPR97NZuj0ekHx/+KoCFgqZDhhdJ1YbNRmMhgMmL/hEvKMa0oH0yuxdOd1fDI1Dl8ezcFWo2hccW0rntx4GWdfHIz747wxNNId5fWtCHS2EQiCejtYmXU9YpBT1SSg6lc0tOPr47l45/6uIm2OztY4/4EZMLR2a2fq3iTMEoq8AaSaXsITO7b3FTc4GUhkxHLie45bGhkQzqY1hYS0QQCa3sdO+58vA/UlxE6SSIC4B83v6v/FcNsCPT4+HgsXLkR8fDySk5MRF2dG4Okexn8mRsNWlY6L+TWI83XAy6NpP+pSYa3gvIqGdpTWtcHH0RKfHc7GL1dK4KZWYfGIcMT62IuoogxslHLWqoyPiwW14mP5tQLREAZqlQLzBgbh44M3AMBIP6cPQt7NZuxPK4eHvSVGRrlDIZOiplnoB6/VG1DfqoGzjRJPDg4RWcu1aXT4+lguLhXWokeAI+b0C2D3KFefysMPF4pgb6XAs0PD0CPAEXq9AVNXnGFvKiuO52LTY70wIsodE7t5Y8vlYkgATIr3weBwsfL8P7gFpFKyLWKsiwBK/jZOM3o92gPjlnOUx8YK8v+VSIDoqYCtG3nl6trJh9MlDBi+TDjZ1mlouq2wJMuMo8to7zJ6Mu0E6bREWyq7StSjyHG0u+n6z8QP+HvHzA3nCvHWruto6dAh1sce38xIgIutEnq9AWvP5ONoFtn6zEsKZpuItiqFyDHi3YldYauS44Ix7r5kjLuMMwYDnd6AuhYNks3Fy4JajOzqgfpWDbZcKkZTm5bshZysMCcxAHUtGuxIKYWXvSWWjAqHSiGDwWDA0cwqXC9rQL8QZ5El26H0CmpgutlicncfJIW5CFhQAc7WLAvK094S702K+X+9f5nljfjP3gwU1bRgZFcPPDU4RLTr+Q9uAamUCnfT4r3ogvD7shSKcwY97VBm7qHkcdhblDBa2tOe5Z7FNC1y70rqwAxUavHv0OsBGIQNys0PATf20/+fXg5M3UDT9clryItYoaJ9cr5ivJ03J4D5N8DfOV7yoVLIBPaODJ4eEoLWDi0OXK9AgIs1Xh4dCZlUguSCGnx3Kh9SiQSz+vqzQxu93oCD6RXIrmpCUqgrIj2JpbPyYc5u1GAw4IujOdhyqRiutio8NyIM3XwdzNoIqxQyvDAqAi+YHL/Z1MEW5wwu5lMcNs2PWzU6ZJQ1oGegE2yUckEDQKc3QCqBQDOjsU2DDecKUVrXijExnuju7yjKWwHufvD59G54f18mG7cXmuSw/8AM9Hpgx5NU1AK0JjR2uXk/8n6LaF0ofQdpAA1dSnGuJo/cgVRq2jNX2dHjB78C7HwWFA8VNEgCyHKt4DS59siUwMAX6Pl+LTRttHZ5Yz/lrkPfJPHRhjLg636chsf5FcATZ4R6SX9BSAy/wkvg6NGjyMnJQXBwMAYMGHC70383pKen/2GCIi9uvYYNvG6el70lji8eiA3nC/HKL5y9lZ2lAmdeGMQKGBUa9yoZ5fjOcD6vBpO/PiM4tuaRHhgQKi7QGWSUNyC7sgm9A53gZKPExfwaPMBT5hwU7opVM7uTz/hPHM25m689tszr2+nzPrv5CrZc4uwMpvXwxbIJXbEjpRRPbuSsMawsZDj5/CDcqGjElBVC4bCJ3bzxwWRKVqsa2yGR0E7SP7gD2DyDJkYMVPbAokzalfyqL6eqaeMOPHG686BVlblB7LoAACAASURBVAVsnUuCSZ7dgAnfcN1NPn6Zx/m6A5TUmhND+oviTsSVP2PMvNvxsrqpHb2XHRYweWb09sPS8VH4+GAW20AEaNXn+zn/f7/lLZeK8ezmFPb7GG87bFuQiOulDRi1XGjf9/kD3TAk0hWjl59EdiUJvtko5fhlft9OmTtv7byOlSfzAFDP6qPJsSwd9fuzBXiZF9tHd/XA59O7Qa834GxuNVo6dOgX6iwSgfu10Oj06P/fIyir5zyFl4wMZ1Xt/8FvwPpJXKEMkOrvo4dIj+PUJ9xxp2CybWSKhfYmYhAxtmh6PblM2HoK4+jJj0lQU68Beswl8c+aXODTbsLXETgQmPHL3bjCPxS/Nbb8GeMl8MfmmLdCTlUTRn58go21SrkUB54ZAF8nK7yw5SprhyuVkD6QKaV9/bkCvLSVi2VqlRxnXhgMa6MIW1ZFI+ytFKxLkTkYDAYMfP8oa6MJAGNjPPHptDh8eugGPjjACX9ZW8hw9sXBgkasXm/A0p3XsfF8ISwtZFg0NBQP9faHwWDAfV+cRorR2UciAVbN7I7+IS4Y9tEx5PB24lfOSDArmPcPfgXSdwCbHhQem7IeiBjz6x5flQV8M5AamADgHAY8foJbFarOoVzStzftqvNRV2Rc7fx/7o/z3TMA0jWadxo4tRw48Irw3FHvi50z/iT4tXHl1uaNAA4fPoyrV69i9uzZWLt2LU6e7ESo5G+GJSPDMSbaA5YKGaK97fDF9G6QSSU4lincBa9v1eBKYR20Oj3mr7+E/u8dQf/3juCJ75OhNQZXnd6AXVfL8MnBG7haTEGpR4AjXhoVAQcrBdQqOf49LBQDQl2g0enxy+USLD90Axnlwt2kcHc1xkR7wslY+K46lSdQ5jycUYmsikZMTvDBl9O7YXysJxYODsF3Mzlf4IY2DS4V1rKCJQaDATtSSgW/h/n+iMm1tnTocC632uzeFV+l08VW+U9xfidRZaKAyajAp/4ktLxoKievzM6wbT4FVIBooNvmi8/paCY/Xz4ufPu/ve57FH/XmFlY0yLy6WYK4+1XhDHkZPZNVBt3tzPLG/HpoRvYerkYGpPHm2JCN2989WA87ov1xMJBwVjzCMWuSE813rwvCs42FrBRyjF/YBBGdXXHscwq9jUAtHqz6UKh2edu6dAKdi0NBrAWmAAE9EoA2J1ahprmDkilEvQJdsaQSDdRcW4wGJBaUo/iWqEoU5tGh80Xi/D5kWx2CpVR1igozgGItEX+wf+IsZ+QU4XcEvBL5IQobxwUnledTdobDJQ2XHFeVwh83gP4KhH4MJzUiAGg6DzZvrXXk0XRqY+BjJ1koQYT9oO884Ln74q/a7z8LdibWi6Ite1aPfamUTzadIGz4tUbyL7NFEczhQ4rDW1aXC6sQ01zB8Z+ehL/x96dx0VVrw8c/8zCsC+ygwq4gCKiiLjvoqllluWWpmWWeW/7tc36udxbuWTrtcWysmw1y2uLVi655L4kriiKorgBssi+DDO/Pw4MDIy7MAM879eLl54zZ2a+o/BwnnO+3+e57e1NdJvzJ2+tPmo6Jq9Izzc7TvPBhuMkZ+SjUql4b2w0EYFuaNQq+rXyYcZQpR7O5D7Nua9zEC72WkJ9Xfjw/o7VZkn9+PcZPt+aRJHeQFZ+CdN/OkRCSg4Hz2abknNQ4vA3O06jUav4bnI3pvRpwT0dGvP5xE6SnN+MtKPV910s25d3EbZ9oHxVrexe7u8vKpLz8ucerxRPvVoonYOqJuegzBi6WnKuL4Yzu5UK8uUqX2QF5WLppTPm9UPK1YNYe9Up7gsWLGDJEqUa3zvvvMMjjzxS53pU1gQ3BzveGxtdbX+Yn6tpbTqAVq2iha8LfxxKYeWBigIgvx28wKqDFxjWPpAXftzPD3uU9hjvrEvgg7HRDIkM4JHezXmkt/n0j39+/TdrDitJ13/XHePziZ3pGWq5oIaluRHl+4ZEBlRbn/n7wfM8s3QfBSWleDjZ8ekDMXQM9sTPzYEzmRVr8coLx1mq1B7q50JLX1d6h/mwqaxKsquDlondQyyOUdwCrQYr09vL+UYoa4TUFn681Ve4u3d2z5W3QXlNO0fzwGxfT9qR3CINNWZGBLrj7+bAheyKJDM2XDmB8nd3MGup5uqgxdley+ZjF3lw8U70ZdUjV+6/YDYV05LBbf1NbSQrG981uFoVc62m+vRwjaUpfCix0Vi1j3UlLvbV26NZuhhZLiu/mPGf7uTA2UuoVPBAtxBmDYvAaDQy4dOd7ExSTjz+u+4Y3z/ajRAvZ+y1aooqXVQN85N16reEWyDcb+HipG+4cpJXzsFDKZJkyYa5kF42C6S0GFb/H7QdYTlOnt2jrDmPGlsx20jrAD2eurnPUQ811Hh5M/zdqicf/u6OWJoQa7Cwr5Wfq+k8EpQ16C18nVn01wkOnFU6EpQajPz3z+Pc3aExQZ5OjPpoG4fKimu+/+dxVjzWg7aN3Vn5ZK9qr2+v1TDnnkjm3HP5Ogr7zmRV35ecZbFoZnl7NR9Xe14cIkvpbonQ25QWluWFgFVqZV9eOizspRQOBqVt5D+2gFOVItiWziXVt6h+xIWD8NW9yk0ljT0MfUtZV+7T2rwlm5OX0rq43SiluGZ5YU/vMKVdZx131TvoWq0WV1flBNzV1RX1ZU5uhGJK3xb0KkuYXey1/PuuCPzcHEwtdipLzsjnYm4Ryyv1rjQaMeuNXtnJi3lmQVVvMPLp5opjz2UVsOX4RfKLlXYTE3s0Q1epaF3fVj608recTBkMRmb8dMhU6TMrv4RXflWSvpl3RuBQdgfcSacxrcN/oFuIaQ25g52a5we3Mq0t+uyBGD4e35HZwyP5c2rfasXxxC3U9yXo+S8lMY8YDveVVdCMHGFe/K1RM2VfSYHSP/2ddvD1KLhYdtIZ3N38dUMsLHvQ2iv93suptUrlT2HSUGOmTqtmyaTODGzjR3iAG88NamW6MPfC4NY0clJ+edtpVLx8ezgOdhoWbzlpSs4B1sanVOs/fjN6h/rQvknFlXpPZx3jugRRajDy5uqj9J2/nrGLtrP/TBbO9lrur5LgT650gfTJ2FCzhPwffVpcti8vwOItSaaTXaMRPt+axKFzl9ibnGVKzkG5+/XF1iTcneyYPVxZew/K0qPyWiKihgz8d0XrRydvuOt95QJkqR6StpjPTqpa1b20WDmJrRo3QblLD8rr3b9cWc/++C4I7lYjH6Mua6jx8mYMbR9Aj5YVSyz6hPkwpK0/Xi72jOxYUc9ArTKPYeUe7dOc3mXLJV3stcwaFkGAu6Pl89TMArYmppuSc4C84lK+2n6q2rHXo0sz86V2ahV0CvGkuY8L90ZX1GBwc9Ba/AziJgW0U7oFNe2qfI36Uqm1cfCHiuQclL8f+KH682MeAsdKSXtgtFIsuLK8dEj80/wueGXpifDNaOVcdNVzyrkpKB2McsuKxJYWwe/TlMdue0U5zwUlOR/2Hmh1ShvNKVuU7h73LILJG+vFjaOr3kFv164dU6dOJSoqiv3799OmzWVaOlViMBiYNWsWR48eRafT8eqrrxIcXHHis3HjRt5//32MRiMRERHMnDkTg8HAnDlzOHjwIMXFxTzxxBP069fv5j6dFbg52PHlpC5czC3CxV5r6lU5sI0vb605SkmpcjKqVasY2MYPddWerGBxn7Lf0j5l52ebT/LaqnhKDUYaOSlj6NzMk9+e7sXvBy/Q2MPRrNd4Wk4R+89kEdnYHV83B4pLDaTlmreqKL9rPrCNHzumDeDw+WwiGruZKhc76jR8+mAn0nKKcNRpzE5WtRo1t0XU/SqKNqe0BI6tUU4OwwYpJ5NandJOrWpLNcdGMGWzsj5dpVIKx9m7Kut4dpZNz8w6payVfHwX3P2hUu04eadSwf3Od6u/Pyh3gVr0V3oDh/S03L+3AbvemFmf4mWYnyuLJlS/A96+qQdbX4xl35ksWvi4mHrcqq4Q/06n57PjZDqRTdxvuBWjVqNm6aPd+OPQBXIK9aaT2EWbTrDgT6WlZFJ6PhMX72LLi/2ZMbQNPVp4E38+m15hPkRV6r/bK9SHjc/1ZcvxdFr5uRJZKfEvNRjZeTIDnVZtugNUedZRufK+u1WV/zvc27EJd7QLICu/pFqLS1ED3JvA5A1KEUxHTyWWZp+Hz++oaIfW6WElwQ4fVlEJHpT16r4RSlGkYQtg0xtKfO76DwgdoByjUiltMsVlNeR4eaPstRq+frgrB89eQq1S0SawIj7OuSeSfq19OJ6aS99WvrRt7I7RaGRbYjqpOUX0a+WLu5MdSx7qTHpuEc6VzlOHtPU3a/Xm7aKjc4inxSKclmL3lRTpS9l+IgNfV3vCA9y4s30gJ9Ly+HL7KVzsNTwzMMxUTPnNUe0ZFdOEc5cK6BvmSyNn3Y38M4mrCR9afc25ysIFMkv/141ClPPGwz+BvZsyY0hTKaWM/wV+fFgpLqd1VApjVu52YTTCd2MrOgrt/FipAD9kLlxKNnsrirKhIEt5z39uVaa1O/sq8bqcvYtSqK4euWqCPn36dNauXcuJEycYMmQI/fv3v9pTWLt2LcXFxSxdupS4uDjmzp3Lhx9+CEBubi7z589nyZIleHp6smjRIjIzM9mwYQN6vZ7vvvuOlJQUfvvtt6u8i22rusa6pa8rX0zszCebT2IwGnm4Z3PTFPExnYNMBec0ahWPlhUFSsspYs3hFLxcdMS29iXYy5k72lX0ytRp1Dzcqzm5RXpe/+MIpWV3ojLzS3hj9VE+n9iZFj4u1doErT50gce/3Uux3oCdRsVbo6K4s30gsa39WBtfcYd+aLuKhN7dyY5uLSwXFys/2RY1TF8Enw1W1oeDUv3y4XXVpx5V5uAG0ePN9x1fZ76dfkxZX9ko2PI00LhvlGlOKjX0eBrajVSutDaQNkDX63pjZkOJl446TbX2Pg/3asamhDTTesrbI/0J8XZm1YHzPPHtXlNMmzG0DQ+VVThesi2JL7Ym4aTT8mRsqKmljsFgZOOxNM5kFhDb2pdAD6XdoIOdhruizNfBbUwwX4OZnlfMoXOX6BjsyYA2fpdd2xjg7siIjuYVtnMKSxjz8XbTHaZeod4sfrATQ9sF8GOl2VGNnOzo0dIbF3stPVp6seW4UnHW0U7DxB4hpuMc7DT4u99YoTlxgyq35Nn2nnmv8l2fKHeLujyqbJe3cOv7YkXF4+gJype4bhIvb1zV1pCgtIcc3NZ8mcbj3+w1LbH0cLLjhyndaenrYqpXVG5ou0AKikv58e8zeLvY82RsKI46Dd1aeBHV1IO4srXhrg5axner3hLtXFYB//7lEAfPZtOthRfTh7bB3dGOM5n5jFq4jXNlNTbGdgli9vBInhoQylMDLFdc79K8blfgrrPa3qsUXbtUVnPFI0iZeQlKN4xze5VZQ/5tldZsnSZZfp3fX1KSc1DaVf7xknmCfumMebtfgMSyc9OI4Ur3oHJNu5r3RW8gXS6umqCnpKQQEhJC8+bN+eSTTwgICLhq9bk9e/bQq5eyLiUqKoqDByuqRe7du5ewsDDmzZtHcnIyI0eOxNPTk82bNxMaGsrkyZMxGo1Mnz79ci9fZ3Vv6U33ltXXi792d1tiW/uarni28nfleGoOwz/YSk6hMl29V6g3X07qwn/HdODOdgGczshnQLgfzX1cOH+pgMIS8+JKKdlF1d6n3JzfjpiKx5WUGpm9Kp472wfy9uj2LPjzOAfOXKJ7Cy/ThQJhI46srEjOQbnzHff19VdQ92tjvmbdyevyPSOTd8KKf1RsL39E6QccGHV979mAXG/MbMjxsmtzL/54pjdrD6fQ1NORgW2U78O31ySYknOAd9YmMKFbMJuOpTHjp4o1w//4ag9/Tu1LkJcTz3wfx09lxehes1PzzSNdiQ6qvp4RoLW/q1kfdJ1WfV29hSv7Yc8Zs+mffx27yJ9HUrktwp8PxkWzdFcyjZzs+Ge/lqZZRosf7Mzvhy6Qml3I4Lb+19QzXtSSnAuW9/lFQNcpype4ZSRe1qzD57LN6h9l5Zfw6eYTzLmnncXjR8Y0ZWRMU7N9SoG2rqw6cJ6s/BJujwywOMPniW/3mu62/7DnDPpSA++M6cDHm06YknNQir5N7B4iSx9tkZMnTNkEh/6nbEfco7Sd3PyOUgwTABXc/YFSY+NycqvE0apx1cVPWVaUX6kInW/Z7Jnezymte4+tBp9w82WVDchVE/SpU6fy+OOP88033zBo0CBmz57Nl19+ecXn5Obm4uJScbKj0WjQ6/VotVoyMzPZsWMHK1aswMnJiXHjxhEVFUVmZianT5/mo48+YteuXUybNo2vv/76Cu8CRUVFxMfHX/GYuiIQCPQFQ+YZ4jNhwbY0U3IOyknf8k17CfdxIFgDwT5QdDGZ+LLv7Ug/Bw6kVATA7oHay/7bpF4yn3p5MaeIw4cPo1KpGN4MhjdzB/QkHrNQ5VFYjfupYwRW2Zd29iQX4+NxO7UanwMLUZfkktViOGmRU0zTkrT5qThkHqHAK4JSBy+0zSbQ5Fw8jhmH0Tt4cb7jS+Qes1z3wPvQ95g39jOSun0p6eEya+JyrjdmSryEnj4ARSQcVe7QZOWZx6j8Yj2H4o/w0550s/16g5EfNx+gnb+jKTkHKCwx8ObKffxfX8t3wm9rYmBPgCN7zxfgolMzuZMXKacTOWswsv9CAY52asJ9Kk5A95zN5+Nd6VzM19O3mQtTOntjV1aA7mhS9fV1B46foqk6k2ZaeLGbchJamnGG+EqHhuog1BtyLpwi3kJOKKzDpVE3mlKx5rLEyY/EQm+MNvqzU9fZarwE246Z1+pQSmG1fWdTM0yf63BqIUWlRtr5OaCxtI6yknBHwBEyz50k07wxB4UlhmpT4f+Mv0B8fDwnz1WvBP734WPoMxyv78OI2uNcVi8j6TwYzhC28XUq5nUZKV77Gon2HVDpC/DfMx/XMxsocWnMheipFPhEERA0CI+Tv5iekdl0IBeq/Cw5x7xE4M7X0BamU9AonDPNH0RffkyjAdC5bKnQ6TTAfNZbQ3DVBF2lUtGpUycWLlzIHXfcwffff3/VF3VxcSEvr6Jir8FgQKtV3srDw4PIyEh8fJTT/piYGOLj4/Hw8KBv376oVCo6d+5MUlLSVd/H3t7eJntU3grOB4uBHLN9gU2CCL/MtJ+vgluycFMix1NziW3ty5jOl18XPLKTgc+3Jpm2R8Q0uabaAsLKgv3g8CdKCzUAnQs+sY/jgwq+n2Wqxukd/wXerbsr63H2LYWV/wSDXqmGOfJz6Hg7dNwGuWloHRvRtHzdUFYybHsfCjKUK6PN+4KqHxxcZDYM38hYfEPr588dcNMnZNcbMyVeVvdgTzvm/1FxgXBUpyDat21Dj4Jkforfb3Zs/+hWOOk0gPm6NQdH5yt+3v9FQUZeMc72Guy1GjLyihm5cKupz+6AcD8WTehITpGeEd+uI69YKaC5KiGHiGaBpqVDD3nmsPzwZtM0fTcHLRP6R8nSn7oqPByaNFaW9rj4Ytf9SVo3qj6dF4DCS0rMvJgArW5Xqgk3QDcTM201XkLdipmX06qVkc/25RJ/Xpnlo1GrmDKwLWHNvJj4+S5Tt53W/q4sfbQb7o7XVon7+93JbDyaRpifKw/3aoaTTkOIV6pZX/TIpo0IDw9nosqTvz7fbdof4uXEvb2j0GqkIGCdUKoHY6nZLp0a5Wdj9XRIWgWAJusYIdtfhmcOQ+inynKh5F0Q1JVG3R6jkabK91Z4OPR5AAoycXTxwfJih/rnWuPlVRN0vV7P/PnziYmJYfv27ZSUlFz1RaOjo1m/fj233347cXFxhIVVVKKNiIggISGBjIwM3Nzc2LdvH6NGjSIjI4ONGzcyaNAgjhw5QkDAZVqdNBDjugbzU9w500lf28ZudA7xxGg08texi5y8mEffVj4EeylFNdyd7HhhcPX2E0kX83h7bQLJGfncHhnApJ7N+L87wgnxcmJXUiYdgjx4QFqg1Q1OnjB5Pez5XFmPHj1BWYe+b2lFq4xyp7YqCfqa6UpyDko1zLWzoPXtyrZLpXvjJQXw2SDIPqts7/8eHvhZOensMkXpda5SQefJFQWQhEXXGzMlXlb3WL+WNPN2ZmviRSIbuzOirDLxPdGN2ZmUwf/2nsVOo+KffVvSvqyQW8+W3qZp6xq1igllce3X/ef4avspnHRKf/SOwRU1GzwrFR/6evspU3IOSkX5rYnpqFUqU3JebufJDB4rqzEV5ufK91O68fX2U+i0aib2aCbJeV3XaojyVa44X+ltXlqsFEMq7+H77Vg4Vda3+9D/lIS98yO1P946TOJlzVKXTU//ZsdpUrILGRYVSHRQI9bFp5iSc4AjF3JYtjuZh3tVVEw/f6mAtYdTCHB3pF9rX9Md9oUbE5n7m7J+eOWB88QlZ7J4YmfeHNWep76L40xmAa39XfnPXW0B6N/aj8UTO7Fi71l8Xe15uFdzSc7rEo1WKZa57b2KfeVLfU5vMz82P125YBnQDnpNNX8s85TS2i3jBLQeCt2fVF7bxXyeplCojJYaJ1aSlJTEli1bGDlyJGvXriUyMpKmTZtSXFyMTme5smJ5lc2EhASMRiOzZ89m06ZNBAUFERsby8qVK/n0008BGDx4MJMnT6a4uJiZM2eSmJiI0Whk1qxZREREXHHw8fHxdf7q5pUkpOTwy75zeDnrGBHTFBd7LdOWH+DbnUrxBp1GzeKJnehhYV07KJWF+72xwax1xsw72zCxR7NaGb+oJemJsKAjVO7hPGwBRI2DV/3AUOmEx9ETXjhZ/TUS/oBvqtz9iRqnrDMCKMpVEnSd8y0fvq252bhyvTFT4uX1yykswU6jNlUfBigsKWX532c5k5nPkLYBRDZxZ2viRcYu2mE6xtFOw8bn+uJroZL6rJ8Pmc0sAvjvfR3oE+pDlzlrzep8PD0glKcHSAu0BqE4Hxb1r6jb4d5Uqfxekg/vVCmUGdhBeayBuZnYYqvx8mY/l637fncyz/9gPhtpSp8Wpj7jB85cYvTH28gvuzg5KMKPj8YrHToGvLWR46nm7TD3/N8AvFzsMRiMZOYXVytAJ+o4oxEOr4Czf0Oz3hA6UNn/+zSlB3k5Bw/4VzzonKo///3O5n3MB/wbej5d82O3MdcaV66aoF/OhAkTWLJkyY089Zapz8HTktTsQrrOWUel2kmm4nGWHDx7iaELNpvt69LMk6WPSi/WeufvJbDuFSjKgY4PwKA5SoXhnx6DvV9VHNf9Cbjt1erPP7tHOQmtrMfTSp/gBqam4oq1Y2ZDi5dgOemeP6JdtSJIAPuSs7j3w62mvuzeLjrWP9sXVwc7Vh+6wL9/OUxqTiFD2wUy555Is4sDoh7btxT+N9l838D/QPQD8EaYMjOpXOhtMG5Z7Y7PBtREbLF2vIT6HTMz84rp/+YGMvOVC/g6jZqfn+hhamn51Hd7zep6APzxdG9a+bsy5uNtbD9RUVDDWadh9/8NxFEnMbHBKcyGnx+H+F+VTkB3vFm9HzpA6hH4oEqu0qQTPLy2dsZpQ641rlx1ivvl3GBeL26C3mA0S84BUzX246k5/PuXwySm5tI/3JeXb29DoIcjOo3aNE0eoJl3/b8D2iCVt/kxGs17Vt7xtlIZ88xuCOkBHScqx/y9RLlr7hOm9DVv3BHajYb9S5XnNQpR+vmKW0ZiZu0L8apeHb08Bv66/xy/7jtPgIcDU/q0oH1TD5Y+2pXvdibjbK/loR7NcHVQ1szdFuHPbRH+GI1Gs/6/n20+yVfbT+Go0/D0gDBTyzdRj5QWW97n6KG0Wlv3H8Co3Dnq91KtD6++knhZsxo56/jxH935YmsShSUG7usSZErOAUpKDdWeU77v2dta8eDiXeQW6VGr4NlBrSwm57uTMpj72xEuZBcyvENjnhkQhvoqhehEHePgBqOWmJ97lhQorXnP7YWQntD5UaVNmtZRablWzrO55dcUwE0k6CpLjetFjQr0cGRwhD+/H1JK/qpUMLFHCEajkUeW7OHkRWX95FfbT6PTaJhxZxteviOc2aviKdIbCPNz4cnYhlKGoYGq+nOp1UG3x8z3bfmvsjYd4OhKpYjHxJVwz8fKsfnpENILqhb0EDdFYmbtG90piHVHUvnr2EXUKhjfNZiYEE9+ijvLU9/FmY7blJDGmmf60DHY02yNelWV/w/XHE7hP78eNm3/46s9rH+2L009pWVavdJmGGyYC9llPe0dG0H7+5S/9/oXtLlLWWYU3A3spW3UrSLxsuY193Hh32XrxKsa3zWE1YdSTDOKOod4mvqux4R4suXF/uw5lUGor6vFmJdbpGfi57tM3YgW/HkcL2cdD8oSy/qp8s/rT4/BwR+Vvx9dBdnnYNBrMGQu/PaikqR7hcoFzau44QRdWMd/7+vAT3FnOXkxjwFt/IgOakRyRr4pOS+3+bhS/OOB7iHcHdWY1JxCWvq6mP3Su1RQQlpOES18nOWXYUOyv0qV3FOb4dJZcG8MAe2tMyYhboEzmfnYadT4la0xd9Rp+HJSF06l5+FgpzHt/9/es2bPS0zLY//ZS0SVFZy7FpuPmbd90RuMbEtMlwS9vnFwV9aV7/sG9MUQdR+4N6l43KuF8iVEHXciLZdGTjoaOevo1sKLnx/vyaoD5/F3d+De6CZmx7o72tG/9eVnDO1PzjJrFQxKu2BJ0Os5fTEcWmG+b/9SJUHv+CC0uRtyzoNP6+o3lIQZmeJuw77beZoPNyZiMBp5pFdzJnQLQadVV1s/6efmgJezjvS8iql44QEVU5XcnexwdzK/G7pkWxKvrVTurLf2d+XziZ3xd69eOEnUQ67+kHKgYtvOSZmmdCUJf5RNV+qlTJUX101iZs0p1ht47Ju/WXM4BZUKRsc0Zc49kaYLj+XdLsr5VqmyrlZxxcrr5oh5uQAAIABJREFU3+48zYcbEjFiZHLvFozvGmwWY8u1CbzKz5Gom1x8lKVA1yo3DQ58DyqN0nrN6fKzMoRlEi9rT2ZeMRM/30VcchZ2GhVPDwjjsX4taRPoZhbT0nKK+CnuLFq1irs7NMbDSSnid+DMJWb9coiTF/MYGO7HrGERtPRzwU6joqS04v9R4mMDoNaCszfkplTsc/Wv+Lujh/IlruqqfQ4uXLhgtn3ixAkAWrZsWTMjEgDEJWfx4vIDnErPJzmjgBk/HWL7iXSLx+q0at4c1R4/N+UEMzrIg2lDLl+AID23iFd/VZJzUNprvLsu4bLHi3qm/8tKNXdQTiBjZyhTM0tLlEIfe7+C/IoCMKydpVR43zAHPr8ddn1ilWHXFRIza9+KvWdZc1g5ITAa4btdyWw6dvGyxz/WryWBZRckVSp4tE8LGns4Wjx27+lMpi0/wOkMJRZPX3GQnSczGNGxCSM6NkGjVuGk0/DcoFamKaCigcg8Bbs/g5ObKvblpsLCHvDHS/D7C/BRbyjIst4YbZzES+v7aNMJ4pKV79GSUiNvrD7K6Ur9zEEpUjzk3b94dWU8s345zNAFm8kuLEFfauCRJbvZcyqTjLxilu5O5u21Cfi6OvDa8EjcHe1QqSC2tS+P9pGZJvWeWg2DZoOmrAODnTMMfOXqz8tLh7+/hKO/KX3XxeXvoCckJJCSksIbb7zBc889B0BpaSlvvfUWP/30EzNnzqy1QTZElpLxbYnpdG3uZfH4vq182fpiLDmFJaarmpdzLqvQrHAcUG2KvKgnLp2FIyuVAh1hQ5Sek4Ed4JmDcGYXeLVUpmsaDPDFMDi9VXmesw888ie4BsD2heavuXWB0hNTmJGYaT1J6dXjV9LFPPqEWe6vGuzlzIbn+rH7VAaB7o6EXKF45rbLxOLOzTx5Y2R7Zg2LQKtWSVX3+qA4H+J/UdZIhg+78p3vExvh6xEVReQ6T4bb5yvTOSvfPbqUDIeWQ8xDNTv2Okbipe1IqnL+ZzTCqYw8gioV2Vy+9ywXcys6FpzJLOC3A+fpGNyIC9mFZs/fmqhcHB0V05S7oxpTqC/FzUFq2tRZxXlw+Gcl1rW56+p3wCNHQLM+ykzNwOirH3/xOHw6AAoyle0W/WH8/27N2Ouwyybo2dnZrFq1ivT0dFauXAkoRTvGjh1ba4NryNpZuBPTrsmV785o1KpqyXl6bhGvrYrn71OZdArx5OU7wgkPcKVJI0fOZFZUUxwU4V/15URddy4OFt8OJWW/fMMGw9iyKu06Z2jet+LYkxsqknOAvDTlTnn/6dXXCamuOvGmQZKYaT0D2vjx4cZEymfF6jRq+rXyRV9q4J21x1h18DxBnk5MGxJOK3+lkJdOq6Z7C++rvna7xtVPLirHYhd7KeVSL5QUwqcDIeWgsr1+jrL23C3A8vGb3zKv8L7rU+j9vOX4KDGzGomXtmNQWz9T8WEAL2cdMVWKZVpaLaxSqWjSyAkPJzuyytq1AURWipk6rRqdVr7/66ziPPi4H1w8qmxvfB0e3ahMY78SFx9wqdJurSgX1syAExsgoB3c9ppS+2jHworkHCDxTzi9A4Ist5BuKC57ZhETE0NMTAyHDh0iIiICAIPBgFotP2i1oXtLb54eEMrHm05gNMKDPUKIDb/+Fj7P/bCfP4+kApCUnk9OoZ6F4zvy5aQuvLUmgeSMfIa2C+DB7iG3+BMIq9v+YUVyDpDwO1w4AP6R1Y8tKbS8T2MH3R6HTa9X7O/x9K0faz0gMdN6ooMasfD+jny+JQk7rZopfZoT5OXE++uP89764wCcSMsj4UIOm57vh1Zz7f8nPUO9eSo2lEV/KbH4oZ4h9GvtW1MfRVjL0VUVyTlA7gVluU+f5ywfXzVmGkuVhL3daNj2PmSXFSJsFAIRw2tkyHWZxEvbMbxDE/KKSvnf3rP4uNjz9MDQam3T7oluwmdbTpKSrdxFD/Fy4vbIABzsNLw7pgMvLT/A2awC+oT58PygVtb4GKImHP65IjkHpZtF3DfQ48nrf60/piktfgEyEpUZng+vMW+9Vs7Svgbmqpf+ExMTSUpKori4mPnz5zNp0iQmTZpUG2Nr8J4eEMbj/VpiBOzKTig/+esEH286gVql4p/9WjChWwgAF3OLWLDuGMdSc+nf2peHejRDrVax4Wiq2WuuL9tu5u3Mgvs61ObHEbXNUFJ9X/kdn+xzyhRNnzClB3rLWGW6e7qSzKB1hI4PKH/v/zIEd4fzcRDSG5p0rJ3x11ESM61jUIR/tZlA64+Yx79zlwo5mpJDRKA7mXnFbExII8DdgS6Vlg79b+8Z3lydQEFxKfd3DeaZgWE8MzCMJ/qbx2JRzxgsrHssj6HJu2DbAqVOR+dHlCmYXSZD8vaKY1sPVe4GAUzZrExrV2mg7T1KJXhhkcRL23B/12Du7xps2i41GPnrWBq5RXr6tfLFx9We35/qza/7z6HVqBnaLsA0e6hPmA+bX+hHkd4gS33qG0vnkeX74r6FDbOVpUGdJ0PfF5T9RTmwaT6c/VspLNzzGaXl77G15q9zZqdSn6PjQ0p3ofLzU7+2yvMauKsm6EuWLGHRokX861//YsOGDTz00EMSPGtR5Ts9WxMv8urKeNP2jJ8OERHoRsdgTx5Zspu9p7PKjkunsKSUx/uHEubnypELOabnlE/vFA1Ap0eUq5/lwTSom5KMn9wEX48EfdkdoF5TlUJxk9Yod4wKs6DdGCV5L9ein/Ilrkpipu0I83dl96mKqXOOdhqaejpx+Fw2oz/eZmoDNKJjE94Y2Z7EtFymfr+Psta/vLvuGKF+LgxtF3hdd91FHdTqduVud2aSsu3gDlHjICsZvriz4o5Owh8weT20vVep0XF0lXJxs32lqdlOnlKn4xpJvLQ9BoOR+z/ZYaq/EeDuwIrHeuDn5sD4sptCValUUoejXgofpkxrv5SsbDt6KueHaUdhxT+Asl+WG2aDb2tljfr/psCRX5X9SX8pSybveEN5POdcxWu7NwV7N+Wmz+QNcGCZUv+ow/2glu+lq55x2NsrlcGdnZ3R6XTo9VJdz1p2nsyotm/HyQzOZhWYkvNyv+4/D8Dce9uZqhM39XRk9nAL05tF/RTcDR7dBH1egKHvwP3Llf0bX69IzgG2/Fe5iunkqUxbip1hnpyL6yIx03Y8MyCM6CBlPaSbg5Y590Ti5mDHR5sSzXr0/rDnDCcv5rEnKdOUnJezFHdFPWTvAo+sVyoQ958OU7ZAo2BlaVDl6ZbGUuXCJygzi257Venvq71ycVZhmcRL27P5+EWz4pjnLxXy9fZTVhyRsBpHDyV5vu1V5dxwymZlptDpbZiS83KntoK+SLloWdmhsnPPIa+Dd9nyBxd/uOt9peo7gF8EDJgF3R6TGUdlrnoHPSgoiNGjRzNt2jTee+89WrWStSXW0iGoUbV9UU09aORkh7NOQ15xqWl/k0aOpsf/er4fKTmF+Lk6oFZbKvUh6i2/NspXZcW55tuGEiWoiltCYqbt8HG1Z/k/e5CaXYi7kx32WuWqfF5R9SQgr0hPVFD1gnBRTaVna4Ph5KmcIFbm3rT6cR4W9okbIvHS9liKjzkW9okGwtkbuj9hvq9xTPXjGsco7dVc/M3vlHsEKX96h8LjO5Ulls6+SlchcVlX/deZM2cOeXl5ODs707ZtW3x8LLetETWvT5gPTw8I5ZO/TqJSwZQ+LUxViP9vaBtm/nSI4lIDfm72PDeotel5arWKAHfLPX5FAxTzEPxcKdi2Hgqu11+AUFgmMdP2+Lo5mG2P7RLEuiOppqrvUU09TD3MX7m7LW+vSSC/WM+4LsHcHdW4tocrbEnobRBxT8VdoOZ9lSme4paQeGl7+rbypbGHI2ezlJkjOq2aUTFyUUpU4t8Wbn8D1s+GkgLoNElpr6ZSKe0ml09WihQ7NlJmJVXmFmidMdcxKqPRaLzSAceOHWPmzJlkZ2czbNgwQkND6dfPNtaixsfHEx4ebu1h1Lry/zJVlfZX6blFnM7IJyLQXdpaiCs7tkaZuundCqIngJ3D1Z/TQNxsXLHVmNlQ4+Xl7DyZwcr95wj0cGRslyBcq/TpNRiMMuNIVLh4XCliVHVGkrip2GKr8RIadsxMzSnkmx2nySnUM6JjE8ID3Kw9JGGrDIaKqerlCi9BWoIydV3nZJ1x2ahrjStXzeJeffVV5syZQ6NGjRgxYgQLFiy4JQMUN06lUlVLzgG8XOzpENRIknNRoSATEtdDfpV1tKED4Y43lUrEkpzfUhIzbUtekZ4txy+Skm3eFqtzM0/+fVdbHu3TolpyDkhyLsx5t5TkvAZIvLRNvq4OPD0gjOlD25iS82MpOexKyqC0aqEO0bBZao3o4A5NO0lyfhOuaQFAcHAwKpUKT09PnJ2da3pMQohbIeEPWPYglOSD1gHu/QTC77T2qBoEiZm2IS45iwc+28mlghK0ahWzhkWYtRISQlifxEvb99yyfSzbcwaAUF8XvpvcFS8XeyuPSoj666q3Wt3d3fnuu+8oKChg5cqVuLtLdT0h6oQ/XlKSc1Cqtv/+UsVjRiNknFDWDolbSmKm7Xj99yNcKlDaDOoNRub+doTCkopimskZ+eRK8SMhrEbipe2LS84yJecAx1Jz+WJrkvUGJGyfvhjSE8FQevVjhUVXvYMeFhbG2bNn8fT05ODBg3h6etbGuIQQNyvngvl2btl2xkn4dgykHVGmId35LkQMr/3x1VMSM21H1WntuUV609fDX+wmLjkLRzsNL90Rzni5sy5ErZN4afuqxlFln3R+EZdxYiP8OEnpf+4eBGO+goD21h5VnXPZBH3ZsmX88MMPJCYm0qJFCwB27959TT0qDQYDs2bN4ujRo+h0Ol599VWCgytOfjZu3Mj777+P0WgkIiKCmTNnAtC7d29CQkIAiIqKYurUqTfz2YRo2NqNgt2fVWxHjlT+XDtTSc5BKeTxy9MQNkTWot+kG42ZEi9rzvAOjXljdYJpu1eoN94u9sz6+RBxyVkAFJSU8p9fDjE4wh8fV5myKURtkHPMuqNnSyVuXsxVknKVCu6Kkkrc4jJ+eVJJzgEunYZVz8OkP6w7pjrosgn6XXfdRbdu3fjoo4+YMmUKAGq1Gi8vr6u+6Nq1aykuLmbp0qXExcUxd+5cPvzwQwByc3OZP38+S5YswdPTk0WLFpGZmUlOTg4REREsXLjwFn00IRq4wfPAIxhOb4MmnSr6WF48Zn5cYRbkpkAjuYN4M240Zkq8rDmP9WuJu5OOjUdTCfNzZUpfJRFITMs1O66k1MjpjDxJ0IWoJXKOWXc422v5YUo3Ptp0gksFxYyMaUr3lt7WHpawRfoiyEwy33fxqFWGUtddNkHX6XQ0adKEV1555bpfdM+ePfTq1QtQrlIePHjQ9NjevXsJCwtj3rx5JCcnM3LkSDw9Pdm+fTspKSmMHz8eBwcHpk2bRvPmza/4PkVFRcTHx1/3+IRoMLwGKV8Ax04A4OMVg3fqYdMhhe4tOHkhHy7Iz9LNuNGYKfGyZsW4Q0xnF8DI2ZPHOQtENDLyV6VjPB012OVcID4+xUqjFKJhkXPMumdCuBbQgiGd+Ph0aw9H2Kimfp1xSdlp2s7y68Z5+Tm6btdUxf165ebm4uLiYtrWaDTo9Xq0Wi2ZmZns2LGDFStW4OTkxLhx44iKisLHx4fJkyczZMgQdu/ezXPPPcePP/54xfext7dvsD0qhbhhoW/CRh+lyrtPKxxiZxIud89NavuETOJl7Wvd2oiH1wl+2af0QX/2tla08ne19rCEqJMkZgohTIK+hTUz4dxeCOmJR+wMPOxdrv68BuJa42WNJOguLi7k5eWZtg0GA1qt8lYeHh5ERkbi4+MDQExMDPHx8fTr1w+NRmPal5qaitFotNjvWwhxE7Q6iJ2hfAmrk3hZ+1QqFVP6tGBKnxbWHooQ4jpJzBTChjl7w93vW3sUdd5V26zdiOjoaDZt2gRAXFwcYWFhpsciIiJISEggIyMDvV7Pvn37aNmyJe+99x5ffPEFAEeOHCEgIEACpxCi3pN4KYQQ105iphCivquRO+gDBw5ky5YtjBkzBqPRyOzZs1m8eDFBQUHExsYydepUHn74YQAGDx5MWFgYkydP5rnnnmPjxo1oNBrmzJlTE0MTQgibIvFSCCGuncRMIUR9pzIajUZrD+JGxcfHy/ogIcQtVV/jSn39XEII66qvsaW+fi4hhPVca1ypkSnuQgghhBBCCCGEuD6SoAshFAYDnN4O5/dbeyRC2JzUnEL+OpZGdmGJtYcihBBC1B1yfnndamQNuqh5mXnFFOkN+Ls7WHsooj4ovARf3Ann9ynbrYfCqC9BLdfwRN1xOj0fb1cdTrpb+6vtxz1neHH5fkpKjTjrNCx6IIbuLbxv6XsIIYQtyCksISu/hKaeTtYeiqgPCrPhi6Fyfnmd5F+nDpqzKp5Or62l65x1PPzFbgpLSq09JFHX/b2kIngCHPkVTvxpvfEIcR3OXypgyLt/0Xv+ejq/to4f9py5Za+tLzXw2qp4SkqVci15xaXM/e3ILXt9IYSwFV9sTaLTa2vp9fp6hn+whfTcImsPSdR1cn55QyRBr2P+Pp3JR5tOoDcoJ4tr41P4fneylUcl6rycCxb2pdT+OIS4AW+uTiD+fDYAuUV6pq84eMumopeUGsnKLzbbl5otJ61CiPolJbuQV349TGGJAYC9p7P4YEOilUcl6rxcOb+8EZKg1zGJqbnXtE8IAHJT4eweKNVf+bjIEaCuNC3YwR3CBtfs2IS4RRLTzGNgQUkp57MKb/j1jqfmmF7TUadhUIS/2ePDoxvf8GsLIYQtOpWeb7r5U65ybD2Tmc+hc5dqe1iirmt7r5xf3gBZg17H9Ar1wV6rpkhvMO2LDfez4oiEzdr2PqyZCYYS8AiGCSvAszmUlsCmNyDhd/BpBbEzILADTPgJdi8GnRN0fQycvaz9CYS4JgPC/dh7Osu0HezlRKivC5l5xcxffZT9Z7Lo0syLqbeFXXF9erHewJSv9vDnkVQAbmvjxwfjonlzVHta+7tx4Owlerb0Yny3kJr+SEIIUavaNXHHx9WetJyKGULl55ezfj7EF9uSMBqV4758qAvuTnZWGqmwCWf3wMbXIT8DosdD9ATLx8n55Q2RBL2O8Xd34POJnXl//XFyi/SM7xpM7zAfcov0fL7lJMdTc+kf7sew9oHWHqqwprx0WDtLSc4Bsk7Bhrlwz8fKn3+9oew/Hweph2HKZgjpqXwJUcdM6dOCUoOR3w9eIMTbiecGtUatVvHM93FsOJoGwMGz2WQXlDB/ZHsA1h9J5Zf95wh0d+Shns3wdNax8sA5U3IOsPpwCqsPp3B7ZABPDQi1ymcTQoja4GCn4ctJnXnjjwQuZBcwrH0g93cJ4uDZS3y+Ncl03P4zyrbExAasIBOW3A1FytIyzuwEx0YQfifoi2HXIji3Vzmn7DBBzi9vgCTodVC3Fl50a2F+9ekfX+3hr2MXAVgRd47MvGIe6B5ihdEJm5B7AUrN182SdVr58+gq8/0XDiiPeQTVztiEuMU0ahVPxobyZGzFCWOx3sDGhDSz49bEK+vefjtwnn98/bdp/9r4FH57qhdnMgqqvfaZzPwaGrUQQtiW1v5ufPJAjNk+SzFQ4mIDd/KviuS83JFVSoL+69MQ97Wy78Ay5fwydkbtj7GOkzXo9cCFS4Wm5LycFI5r4HzbgE9r830Rw5U/vVqY73dwB2ef2hmXELVEp1XTpJGj2b5m3s5A9fh45EIOB85eYlBbf+w0KrPXGNjGfP25EEI0JD1aeuNRZTr7He0CrDQaYRO8WlrY10K5e75/qfn+vV/VzpjqGUnQ6wEnew32WvP/Sk9nnZVGI2yCSgX3L4eOD0KzPnDHm9B5svLYgH9XBFedK9zxFtg5XvalhKir5gxvR6OyE0t/Nwf+PSwCgEZV4qNKBY2cdIT5ufLFQ50Z2MaPQRF+fDWpiympF0KIhsjVwY6lk7txV1QgfcJ8+GBcNH1b+Vp7WMKa/NpA7+dBXXbhJqQXdHlUKQbn4GF+rJOsN78RMsW9HnBzsOPJ2FDm/3EUABd7LU8PCLPyqITVuTeGO9+tvt+rBTy+G9KPg1sg6CQBEfVTz1Bvtr8US3JGPiFezmg1yoXMf/ZtyaaENC7mKstAHugWQlNPJwC6t/Cmewtvq41ZCCFsTSt/V94d08HawxC2pP/L0GUKFOdAo5CK/QP/DT8/AUYDaOwhdqbVhliXSYJeTzzWryWDIvxJTMulazMvqa4prkylAm8p8CLqP3uthpa+rmb7Wvq6sOn5fmw9nk6ghyNtAt2sNDohhBCijnL2ql6RvcP90Kw3nN8HTTqDq3SauhGSoNcjLX1daOnrYu1hCCGEzXPSaRnQRk4chBBCiFvKI0gKD98kWYMuhBBCCCGEEELYAEnQhRBCCCGEEEIIGyAJuhBCCCGEEEIIYQMkQRdCCCGEEEIIIWxAjSToBoOBGTNmMHr0aMaPH8+pU6fMHt+4cSOjRo1i5MiRzJo1C6PRaHosMTGRjh07UlRUVBNDE0IImyLxUgghro3ESyFEQ1AjCfratWspLi5m6dKlTJ06lblz55oey83NZf78+SxcuJBly5bRuHFjMjMzTY/NmzcPnU5XE8MSQgibI/FSCCGujcRLIURDUCNt1vbs2UOvXr0AiIqK4uDBg6bH9u7dS1hYGPPmzSM5OZmRI0fi6emJ0Whk+vTp/Otf/+Kf//znNb1PUVER8fHxNfERhBCiVki8FEKIa1Nb8RIkZgohrKdGEvTc3FxcXCr6cWs0GvR6PVqtlszMTHbs2MGKFStwcnJi3LhxREVF8euvv9KnTx9at259ze9jb29PeHh4TXwEIUQDVdsnZBIvhRB1WW3GzNqKlyAxUwhx611rvKyRBN3FxYW8vDzTtsFgQKtV3srDw4PIyEh8fHwAiImJIT4+np9//hl/f39+/PFH0tLSeOihh/j6669rYnhCNGyGUtjyDiT8AT6toO9L4BZg7VE1WBIvra+k1MAH6xPZdCyN1v6u/GtgGF4u9tYelhCiComXNW9XUgYfbUykSG9gQrcQBrbxs/aQhGhwaiRBj46OZv369dx+++3ExcURFhZmeiwiIoKEhAQyMjJwc3Nj3759jBo1ijVr1piO6d+/P5999llNDE0IsfF12Fi2bi95B1w4AJM3WHNEDZrES+t744+jfLTpBAB7TmVyLDWX7x/tZuVRCSGqknhZs85mFXD/Jzso0hsA2Hz8Ij/+ozvRQY2sPDIhGpYaSdAHDhzIli1bGDNmDEajkdmzZ7N48WKCgoKIjY1l6tSpPPzwwwAMHjzYLMAKIWpY/C/m2+f2QtZp8AiyzngaOImX1vfbwQtm2ztPZpCRV4ynsxSUEsKWSLysWeuPpJqScwCjEf44dEESdCFqmcpYuQdFHRMfHy/rg4S4Xt+OhaMrK7bt3WDqUdA5WW9MNqS+xpX6+rluhfGf7uCvYxdN257OOna8FIudpkYanQhRr9TX2FJfP9eV/HUsjfGf7jTbN3t4JGO7yAV8IW6Fa40rcvZRBxkMRvSlhqsfKIQlA2ZV3C23c4Ih8yQ5F/Vasf7K8XLakHAC3B0AcNZp+M9dEZKcCyEanJ4tvRkV0wSVStnu28qHe6IbA2A0yrmnELWlRqa4i5rz+ZaTvL32GIUlpdzXOYgZQ9ugVqusPSxRl/iEwZNxkHoY3JuCo4e1RyREjTiemsszS+M4cPYS7Zq48/boKFr4uFQ7rk2gG38934+jKTkEeTrh6mBnhdEKIYR1qVQqXh/RnqcGhFGsN9DM2xmApbtO8/rvR8kuLOHe6Ca8cndbuYgpRA2Sn646JP58NrN+OcylghKK9AY+35rE//aeBZQ7REt3nea1lYfZmnjxKq8kGjy1BvwjJTkX9drzP+zjwNlLAOw/c4nnf9hvemxfchZzfovni61J5Bfr0WrURAS6S3IuhGjwGns4mpLz0+n5TFt+gPS8YkpKjXy3K5lvdpy28giFzTIY4MAP8MfLcPQ3a4+mzpI76HXI/jNZFvfd27EJz3wfx8r95wFY9NdJ3h7dnuEdmtT2EIUQwmbsO3PJbLs8hm5KSOPBxTsxlFVgWbn/PN9PkartQghR1YGzl0yxstw+C+ejQgCw+mXY/oHy923vwcD/QI+nrDumOkjuoNchnUI8qTqbvWtzL9Jyilh14LzZ/s+3nqrFkQkhhO3p0szTbLtrcy8Avtx+yuyEc2dSBofOmSfzQgghoGNwI+w05ief5bFUCDOlJbC7ShvDHR9ZZyx1nCTodUhzHxfeHh1FM29nfF3tefa2MIZEBmCnUaFRmQdPB6381wohGrY3RranbysfXB209G3lw+sj2gHgYKepdqylfUII0dD5uzvw3thoWvq64O2i47F+LRjZUWZoCgtUatDYm+/TOlhnLHWcTHGvY+6KasxdUY3N9nk46XiwewifbD4JgJ1GxWP9WlpjeEIIYTMCPRz5fGLnavsn92rOn/Ep5BWXAnBn+0CLxeOEEELAoAh/BkX4W3sYwtapNdD7WVgzvWyHCvq8YNUh1VWSoNcT/ze0DQPb+HE8LZfeoT409ZS2WUIIYUlkE3fWP9uXdUdSCfRwpFdLb2sPSQghhKj7ejwJIT3h3N8Q3BN8W1t7RHWSJOj1SJfmXnSRdUFCCHFVvm4O3Nc5yNrDEEIIIeqXxtHKl7hhslBZCCGEEEIIIYSwAZKgCyGEEEIIIYQQNkASdCGEEEIIIYQQwgZIgi6EEEIIIYQQQtgASdCFEEIIIYQQQggbIAm6EEIIIYQQQghhAyRBF0IIIYQQQgghbIAk6EIIIYQQQgghhA3Q1sSLGgwGZs2axdGjR9HpdLz66qsEBwebHt+4cSPvv/8+RqORiIgIZs4FwAFnAAAQsElEQVScSUFBAVOnTiU7Oxs7OzvmzZuHn59fTQxPCCFshsRLIYS4NhIvhRANQY3cQV+7di3FxcUsXbqUqVOnMnfuXNNjubm5zJ8/n4ULF7Js2TIaN25MZmYm33//PREREXz99dcMGzaMRYsW1cTQhBDCpki8FEKIayPxUgjRENTIHfQ9e/bQq1cvAKKiojh48KDpsb179xIWFsa8efNITk5m5MiReHp68uCDD1JaWgrAuXPncHNzq4mhCSGETZF4KYQQ10bipRCiIaiRBD03NxcXFxfTtkajQa/Xo9VqyczMZMeOHaxYsQInJyfGjRtHVFQUzZo1Q6PRMGHCBBISEli8ePFV36eoqIj4+Pia+AhCiAaqqKioVt9P4qUQoi6rzZhZW/ESJGYKIW69a42XNZKgu7i4kJeXZ9o2GAxotcpbeXh4EBkZiY+PDwAxMTHEx8fTrFkzAJYsWUJiYiKPPvooa9euveL7REVF1cTwhRCi1ki8FEKIa1Nb8RIkZgohrKdG1qBHR0ezadMmAOLi4ggLCzM9FhERQUJCAhkZGej1evbt20fLli356KOPWLFiBQDOzs5oNJqaGJoQQtgUiZdCCHFtJF4KIRoCldFoNN7qFy2vspmQkIDRaGT27Nls2rSJoKAgYmNjWblyJZ9++ikAgwcPZvLkyVy8eJEXXniB4uJiSktLmTp1Kh07drzVQxNCCJsi8VIIIa6NxEshRENQIwm6EEIIIYQQQgghrk+NTHEXQgghhBBCCCHE9ZEEXQghhBBCCCGEsAGSoAshhBBCCCGEEDZAEvR6YN++fYwfPx6AU6dOcd999zF27FhmzpyJwWCw8uiELRk+fDjjx49n/PjxTJs2jbi4OEaOHMmYMWN47733rD08IWrMtcTJ9957jxEjRjBmzBj2799vzeEKKygtLWXatGmMGTOG++67j4SEBPleEQ2SwWBgxowZjB49mvHjx3Pq1Cmzxz/77DPuuece7r33XtasWWOlUQpbVvl3rrh+NdIHXdSeRYsW8fPPP+Po6AjAnDlzePrpp+nSpQszZsxg3bp1DBw40MqjFLagqKgIo9HIl19+adp31113sWDBApo2bcrkyZM5fPgwbdq0seIohbj1riVOBgYGsnPnTpYtW8b58+d54okn+PHHH608clGb1q9fD8B3333Hjh07ePvttzEajfK9IhqctWvXUlxczNKlS4mLi2Pu3Ll8+OGHAGRnZ7NkyRJWr15NQUEBd999t5xnCjNVf+eK6yd30Ou4oKAgFixYYNo+dOgQnTt3BqB3795s3brVWkMTNubIkSMUFBTw0EMPMWHCBHbt2kVxcTFBQUGoVCp69uwp3y+iXrqWOLlnzx569uyJSqUiMDCQ0tJSMjIyrDVkYQUDBgzglVdeAeDcuXO4ubnJ94pokPbs2UOvXr0AiIqK4uDBg6bHHB0dCQwMpKCggIKCAlQqlbWGKWxU1d+54vrJHfQ6btCgQZw5c8a0bTQaTcHS2dmZnJwcaw1N2BgHBwcmTZrEyJEjSUpK4pFHHsHNzc30uLOzM8nJyVYcoRA141riZG5uLh4eHqZjyvd7enrW+niF9Wi1Wl544QXWrFnDf//7X7Zs2SLfK6LByc3NxcXFxbSt0WjQ6/VotUraEBAQwB133EFpaSmPPvqotYYpbFTV37ni+kmCXs+o1RWTIvLy8swSMNGwNWvWjODgYFQqFc2aNcPV1ZWsrCzT4/L9IhoKS3HSxcWFvLw8s/2urq7WGJ6wsnnz5vHss88yatQoioqKTPvle0U0FFW/xw0Ggyk537RpE6mpqaxbtw6ASZMmER0dTbt27awyViHqI5niXs+0adOGHTt2AEoQjYmJsfKIhK344YcfmDt3LgApKSkUFBTg5OTE6dOnMRqNbN68Wb5fRINgKU5GR0ezefNmDAYD586dw2AwyB3RBmbFihV89NFHgDKNV6VS0bZtW/leEQ1OdHQ0mzZtAiAuLo6wsDDTY+7u7jg4OKDT6bC3t8fV1ZXs7GxrDVWIeknuoNczL7zwAtOnT+ett96iefPmDBo0yNpDEjZixIgRTJs2jfvuuw+VSsXs2bNRq9U8++yzlJaW0rNnT9q3b2/tYQpR4yzFSY1GQ0xMDKNHjzZVMBYNy2233ca0adMYN24cer2el156iRYtWsj3imhwBg4cyJYtWxgzZgxGo5HZs2ezePFigoKCiI2NZevWrYwaNQq1Wk10dDQ9evSw9pCFqFdURqPRaO1BCCGEEEIIIYQQDZ1McRdCCCGEEEIIIWyAJOhCCCGEEEIIIYQNkARdCCGEEEIIIYSwAZKgCyGEEEIIIYQQNkASdCGEEEIIIYQQwgZIgi5q3OOPP27tIVjUv39/ioqKavU9x48fT2JiIsuXL2fdunUAfPXVV7U6BiGEEEIIYe7FF1809X+vTQsWLODbb7+t9fcVtksSdFHj3nvvPWsPwebcc889xMbGAvDhhx9aeTRCiIYmKyuLX375BYCPP/6Y/fv33/BrFRUVsWzZsls1NCGEEKJB01p7AKLuW758OevXr6ewsJC0tDQmTJjAunXrOHbsGM8//zwzZ85ky5YtjB8/ntatW3Ps2DFyc3N59913ady4scXX3LNnD/PmzUOr1eLo6Mi7774LwMsvv0xOTg6pqamMHTuWsWPHMn78eFq1asWxY8dwcnIiJiaGzZs3k52dzWeffca6detYu3YteXl5ZGZm8thjjzFo0CDTe50/f57p06dTVFSEvb09r7zyCp6enjz11FPk5uZSUFDAM888Q8+ePS2O9eTJk0ybNg2tVovBYODNN9/k9OnTLFy4ELVaTVpaGqNHj2bcuHGm5yxYsABvb2+ysrK4dOkSs2bNYtasWbfuP0UIIa7g6NGj/Pnnn9x5551Mnjz5pl4rLS2NZcuWMXLkyFs0OiGEuHWqnqeNGjWKjRs38vbbbwPQo0cPtmzZAsA333zDp59+SmlpKa+99hr+/v4Wzwe/+uorVq9e/f/t3X1Mze8fx/HnOSqkcpebtiy6GWIhzKgxaSgMrRuabkicLM40ablt7iYy/0Slg9CobGZ2ZCO24w+kxjCkFWnCIZQO3axO3z/MmeqE72+h78/78dfZ+Vyf67q6/ji93tfncz6H+vp6+vfvT2pqKlqt9rt52NfXl1mzZjFu3DgqKytxc3Nj9+7dbeZ64MABiouLMRqNREZG4ufn99vXS/x5UqCLLvHp0yeOHTvGxYsXycrKIi8vj8LCQk6ePNmmnYeHB5s3b+bgwYNcvHix02BYUFCAn58fERERXLt2jY8fP/LhwwfmzZvH7Nmz0ev1hIWFERoaaup3y5YtREVF0atXL44fP05CQgJFRUUA1NfXc/z4cd6/f09QUJDp6jVAcnIyYWFhzJgxg5s3b5KSkoJKpaKmpgaNRsO7d++oqKjo9G+/ceMGHh4exMfHU1xcTF1dHQB6vZ7z589jNBpZsGABc+fO7XBuTEwM2dnZUpwLITp17tw5dDodDQ0NVFZWEh0dTUBAgNm2p06dQqvVolAo8Pf3Jzw8nMuXL5OZmYmFhQWDBw/m4MGDpKenU1JSQm5uLnfv3sXf35/q6uofhktzoTQ9PZ2ysjJSU1MJDw8nPj4eg8FAS0sLarWaqVOnMn/+fIYPH46lpSXLli3rsAFrY2Pzm1dVCPG3aJ/TysvLO23r6enJqlWr0Ol07N+/H7Va3SEPGo1GampqyMrKQqlUEhUVxYMHD4Dv52FfX1/0ej1qtRonJyfUajUFBQWmsXU6HS9evODMmTM0NjYSHByMl5cXdnZ2v3yNRPciBbroEqNHjwbA1tYWFxcXFAoFffv27fAdb3d3dwCGDh1KdXV1p/2pVCrS09OJiIhgyJAheHh4YG9vz4kTJ7h8+TI2NjY0Nzeb2o8ZMwYAOzs7XF1dTa+/jj958mSUSiX29vbY2dnx/v1707mlpaVkZGSg0WhobW3FwsICNzc3QkJCiIuLo7m5mbCwsE7nGhgYSGZmJitXrsTW1pb169cDMGHCBKysrABwc3OjsrLy5xZTCCHaMRgMHD16lIqKClQqldkCvaysjPz8fE6fPg3A8uXL8fb2RqvVEhUVxdy5czl//jwGgwGVSkVOTg4hISHcvXvX1Mf3wqWPj4/ZUKpSqSgtLSU2Npbk5GSmTZtGREQEer2epUuXcvXqVT5//syaNWtwd3cnOTm5wwasFOhCiF+lfU7z8vJqc7y1tdX0etKkScCXDLdv3z6zeVCpVGJpaUlcXBzW1ta8fv3alEl/lIcdHBxwcnIyjfHs2TPT2KWlpTx8+NCUOZubm6mqqpIC/S8kBbroEgqFokv7u3DhAosXLyYhIYGMjAzy8vKoq6tj/PjxhIaGcuvWLXQ63U/39/DhQwCqq6sxGAwMHDjQdMzZ2ZkVK1bg6elJeXk5RUVFPHnyhE+fPnHkyBHevHnDkiVLmDlzptm+r169ysSJE4mNjUWr1aLRaFi0aBGPHz+mpaWFpqYmysrKTB/I7X37j0EIIcwZNWoU8CXcNTU1mW1TWlrKy5cviYyMBKC2tpbnz5+TmJhIRkYG2dnZODs74+vr2+k43wuX3wulX5WXl7NgwQIAhgwZgo2NDe/evQNgxIgRgPkNWCGE+FXa57Tc3FxT9qqqqqK2ttbU9v79+3h6elJcXIybm5vZPOjg4EBBQQFnz56lvr6egIAAU38/ysN6vZ63b98yaNAg7ty5w8KFC3n06BHwJY9OmTKFnTt3YjQaOXz4MMOGDftFqyK6MynQRbf09Zb13r17o1Qq2bFjB1VVVezatYv8/HxsbW3p0aNHp0G1verqaiIiIqirq2P79u306NHDdCwhIYGkpCQaGxtpaGhg8+bNDB8+nEOHDnHp0iWMRiPr1q3rtO+xY8eSkJBAWloaRqORxMREDAYDzc3NREdHU1NTQ0xMDAMGDDB7vouLCxs2bCAlJeXfLZIQ4q/xM5ugzs7OuLq6otFoUCgUZGVlMXLkSHJzc1m7di0DBw5k27ZtXLlyBUdHR4xG478ap6SkxGwoVSqVpr5cXFwoLi7G3d0dvV7Px48f6devHwBK5Zfn0prbgO2uv/YhhPjva5/TNm7cSFpaGkFBQbi4uODo6Ghqe+/ePcLDw1EoFOzZswd7e/sOedDJyYnevXuzZMkSAAYNGsSbN29+ai5WVlbs3LmTV69eMW7cOHx8fEwFuo+PD7dv3yY0NJTPnz/j6+srdxf9pRStcvlO/J87d+4cT58+ZcOGDb9tzMLCQnJyckwPIBFCiP/Vt59hjY2N+Pn5ce3aNbNtNRoNBQUFNDU14eHhwdatW9HpdBw+fJg+ffpgbW3Nnj17aGpqIjIykpCQEEpKSkzfQf86zvXr18nPz2fv3r08fvyYlJQUUlNTWb16tWlj1MrKisDAQObMmUNwcDDe3t5ER0ezadMmamtraWhoQK1WM336dHx8fLh06RI9e/bk3r177N69u80GrFwlEkL8Db59IJ0QnZECXfxRsbGxbW4tArCxsenSnx7rqgI9KSnJ7INFMjMz6dWrV5v3pEAXQgghhBDfkgJd/Awp0IUQQoj/kNzcXLRabYf34+LimDBhwh+YkRBCCCG6ihToQgghhBBCCCFEN6D80xMQQgghhBBCCCGEFOhCCCGEEEIIIUS3IAW6EEIIIYQQQgjRDUiBLoQQQgghhBBCdAP/AHM3pECjp0wXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(14, 6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, param in enumerate(plot_params):\n",
    "    sns.swarmplot(x=param, y='test_score', data=plot_data, ax=axes[i])\n",
    "    axes[i].set_ylim(.63, .69)\n",
    "    \n",
    "fig.suptitle('Mean Test Score Distribution by Hyper Parameter', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.9)\n",
    "fig.savefig('mean_test_scores_by_param', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy-encode parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:37.903803Z",
     "start_time": "2018-10-23T15:18:37.889091Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 19 columns):\n",
      "test_score                    576 non-null float64\n",
      "learning_rate_0.01            576 non-null uint8\n",
      "learning_rate_0.1             576 non-null uint8\n",
      "learning_rate_0.2             576 non-null uint8\n",
      "max_depth_3                   576 non-null uint8\n",
      "max_depth_6                   576 non-null uint8\n",
      "max_depth_9                   576 non-null uint8\n",
      "max_depth_12                  576 non-null uint8\n",
      "max_features_0.8              576 non-null uint8\n",
      "max_features_1                576 non-null uint8\n",
      "max_features_sqrt             576 non-null uint8\n",
      "min_impurity_decrease_0.0     576 non-null uint8\n",
      "min_impurity_decrease_0.01    576 non-null uint8\n",
      "min_samples_split_10          576 non-null uint8\n",
      "min_samples_split_50          576 non-null uint8\n",
      "n_estimators_100              576 non-null uint8\n",
      "n_estimators_300              576 non-null uint8\n",
      "subsample_0.8                 576 non-null uint8\n",
      "subsample_1.0                 576 non-null uint8\n",
      "dtypes: float64(1), uint8(18)\n",
      "memory usage: 14.7 KB\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=False)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Regression Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now explore how hyperparameter settings jointly affect the mean cross-validation score. To gain insight into how parameter settings interact, we can train a DecisionTreeRegressor with the mean test score as the outcome and the parameter settings, encoded as categorical variables in one-hot or dummy format. \n",
    "\n",
    "The tree structure highlights that using all features (max_features_1), a low learning_rate, and a max_depth over three led to the best results, as shown in the following diagram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:42.804203Z",
     "start_time": "2018-10-23T15:18:42.798964Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 max_depth=4,\n",
    "                                 min_samples_split=5,\n",
    "                                 min_samples_leaf=10,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None,\n",
    "                                 presort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:43.522786Z",
     "start_time": "2018-10-23T15:18:43.504753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=10,\n",
       "           min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T15:18:47.962438Z",
     "start_time": "2018-10-23T15:18:47.930469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1632pt\" height=\"477pt\"\n",
       " viewBox=\"0.00 0.00 1632.00 477.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 473)\">\n",
       "<title>Tree</title>\n",
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       "<!-- 0 -->\n",
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       "<title>0&#45;&gt;1</title>\n",
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       "<text text-anchor=\"middle\" x=\"764.6578\" y=\"-385.0865\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n",
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       "<!-- 16 -->\n",
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       "<title>16</title>\n",
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       "<text text-anchor=\"start\" x=\"1081\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">learning_rate_0.2 ≤ 0.5</text>\n",
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       "<!-- 0&#45;&gt;16 -->\n",
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       "<title>0&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M973.5855,-404.2187C1001.7421,-392.5985 1034.0332,-379.272 1063.2831,-367.2006\"/>\n",
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       "<text text-anchor=\"middle\" x=\"1063.0669\" y=\"-382.7138\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n",
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       "<!-- 2 -->\n",
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       "<title>2</title>\n",
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       "<text text-anchor=\"start\" x=\"296\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">learning_rate_0.2 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"335.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"321\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 256</text>\n",
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       "<!-- 1&#45;&gt;2 -->\n",
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       "<title>1&#45;&gt;2</title>\n",
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       "<title>9</title>\n",
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       "<text text-anchor=\"start\" x=\"615.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">n_estimators_300 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"656.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
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       "<!-- 1&#45;&gt;9 -->\n",
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       "<title>1&#45;&gt;9</title>\n",
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       "<title>3</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.733333\" stroke=\"#000000\" d=\"M233,-157C233,-157 97,-157 97,-157 91,-157 85,-151 85,-145 85,-145 85,-101 85,-101 85,-95 91,-89 97,-89 97,-89 233,-89 233,-89 239,-89 245,-95 245,-101 245,-101 245,-145 245,-145 245,-151 239,-157 233,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"93\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">max_features_0.8 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"134.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"120\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 128</text>\n",
       "<text text-anchor=\"start\" x=\"124\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.673</text>\n",
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       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.603922\" stroke=\"#000000\" d=\"M424.5,-157C424.5,-157 307.5,-157 307.5,-157 301.5,-157 295.5,-151 295.5,-145 295.5,-145 295.5,-101 295.5,-101 295.5,-95 301.5,-89 307.5,-89 307.5,-89 424.5,-89 424.5,-89 430.5,-89 436.5,-95 436.5,-101 436.5,-101 436.5,-145 436.5,-145 436.5,-151 430.5,-157 424.5,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"303.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">max_depth_12 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"335.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
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       "<!-- 2&#45;&gt;6 -->\n",
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       "<title>2&#45;&gt;6</title>\n",
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       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.694118\" stroke=\"#000000\" d=\"M86,-53C86,-53 12,-53 12,-53 6,-53 0,-47 0,-41 0,-41 0,-12 0,-12 0,-6 6,0 12,0 12,0 86,0 86,0 92,0 98,-6 98,-12 98,-12 98,-41 98,-41 98,-47 92,-53 86,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"18.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 64</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.672</text>\n",
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       "<!-- 3&#45;&gt;4 -->\n",
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       "<title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M124.1027,-88.9777C112.6551,-79.4545 100.2313,-69.1191 88.8455,-59.6473\"/>\n",
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       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.776471\" stroke=\"#000000\" d=\"M202,-53C202,-53 128,-53 128,-53 122,-53 116,-47 116,-41 116,-41 116,-12 116,-12 116,-6 122,0 128,0 128,0 202,0 202,0 208,0 214,-6 214,-12 214,-12 214,-41 214,-41 214,-47 208,-53 202,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"134.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"124\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 64</text>\n",
       "<text text-anchor=\"start\" x=\"124\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.674</text>\n",
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       "<!-- 3&#45;&gt;5 -->\n",
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       "<title>3&#45;&gt;5</title>\n",
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       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.635294\" stroke=\"#000000\" d=\"M318,-53C318,-53 244,-53 244,-53 238,-53 232,-47 232,-41 232,-41 232,-12 232,-12 232,-6 238,0 244,0 244,0 318,0 318,0 324,0 330,-6 330,-12 330,-12 330,-41 330,-41 330,-47 324,-53 318,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"250.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"240\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 96</text>\n",
       "<text text-anchor=\"start\" x=\"240\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.671</text>\n",
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       "<!-- 6&#45;&gt;7 -->\n",
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       "<title>6&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M336.0322,-88.9777C328.0471,-79.9123 319.4138,-70.111 311.4056,-61.0192\"/>\n",
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       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.501961\" stroke=\"#000000\" d=\"M434,-53C434,-53 360,-53 360,-53 354,-53 348,-47 348,-41 348,-41 348,-12 348,-12 348,-6 354,0 360,0 360,0 434,0 434,0 440,0 446,-6 446,-12 446,-12 446,-41 446,-41 446,-47 440,-53 434,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"366.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"356\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 32</text>\n",
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       "<!-- 6&#45;&gt;8 -->\n",
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       "<title>6&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M376.9294,-88.9777C379.6357,-80.5533 382.5462,-71.4934 385.2881,-62.9579\"/>\n",
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       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.266667\" stroke=\"#000000\" d=\"M673,-157C673,-157 537,-157 537,-157 531,-157 525,-151 525,-145 525,-145 525,-101 525,-101 525,-95 531,-89 537,-89 537,-89 673,-89 673,-89 679,-89 685,-95 685,-101 685,-101 685,-145 685,-145 685,-151 679,-157 673,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"533\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">max_features_0.8 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"574.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.0</text>\n",
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       "<!-- 9&#45;&gt;10 -->\n",
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       "<title>9&#45;&gt;10</title>\n",
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       "<title>13</title>\n",
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       "<text text-anchor=\"start\" x=\"711\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">max_depth_3 ≤ 0.5</text>\n",
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       "<title>9&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M714.1773,-192.9465C721.2712,-184.0578 729.0015,-174.3716 736.386,-165.1188\"/>\n",
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       "<title>11</title>\n",
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       "<title>14</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7f5ebe6f1278>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_file = 'results/gbm_sklearn_tree.dot'\n",
    "dot_data = export_graphviz(reg_tree,\n",
    "                          out_file=out_file,\n",
    "                          feature_names=gbm_features,\n",
    "                          max_depth=4,\n",
    "                          filled=True,\n",
    "                          rounded=True,\n",
    "                          special_characters=True)\n",
    "if out_file is not None:\n",
    "    dot_data = Path(out_file).read_text()\n",
    "\n",
    "graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T13:40:08.199563Z",
     "start_time": "2018-10-23T13:40:08.196698Z"
    }
   },
   "source": [
    "Overfit regression tree to learn detailed rules that classify all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T13:39:41.423823Z",
     "start_time": "2018-10-23T13:39:41.416611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 min_samples_split=2,\n",
    "                                 min_samples_leaf=1,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None,\n",
    "                                 presort=False)\n",
    "\n",
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart below displays the influence of the hyperparameter settings in producing different outcomes, measured by their feature importance for a decision tree that is grown to its maximum depth. Naturally, the features that appear near the top of the tree also accumulate the highest importance scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T14:05:16.051454Z",
     "start_time": "2018-10-23T14:05:15.554643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_fi = pd.Series(reg_tree.feature_importances_, index=gbm_features).sort_values(ascending=False)\n",
    "gbm_fi = gbm_fi[gbm_fi > 0]\n",
    "idx = [p.split('_') for p in gbm_fi.index]\n",
    "gbm_fi.index = ['_'.join(p[:-1]) + '=' + p[-1] for p in idx]\n",
    "gbm_fi.sort_values().plot.barh(figsize=(5,5))\n",
    "plt.title('Hyperparameter Importance')\n",
    "plt.tight_layout()\n",
    "plt.savefig('param_importance', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use a linear regression to gain insights into the statistical significance of the linear relationship between hyperparameters and test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T13:41:54.168111Z",
     "start_time": "2018-10-23T13:41:54.137741Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             test_score   R-squared:                       0.277\n",
      "Model:                            OLS   Adj. R-squared:                  0.263\n",
      "Method:                 Least Squares   F-statistic:                     21.38\n",
      "Date:                Tue, 23 Oct 2018   Prob (F-statistic):           1.86e-36\n",
      "Time:                        09:41:54   Log-Likelihood:                 2159.6\n",
      "No. Observations:                 576   AIC:                            -4295.\n",
      "Df Residuals:                     564   BIC:                            -4243.\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:                  HC3                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                          0.6645      0.001    494.647      0.000       0.662       0.667\n",
      "learning_rate_0.1              0.0039      0.001      6.250      0.000       0.003       0.005\n",
      "learning_rate_0.2              0.0001      0.001      0.215      0.830      -0.001       0.001\n",
      "max_depth_6                    0.0029      0.001      3.748      0.000       0.001       0.004\n",
      "max_depth_9                    0.0025      0.001      3.407      0.001       0.001       0.004\n",
      "max_depth_12                   0.0021      0.001      2.765      0.006       0.001       0.004\n",
      "max_features_1                 0.0057      0.001      8.454      0.000       0.004       0.007\n",
      "max_features_sqrt              0.0008      0.001      1.260      0.208      -0.000       0.002\n",
      "min_impurity_decrease_0.01    -0.0003      0.000     -0.635      0.525      -0.001       0.001\n",
      "min_samples_split_50           0.0006      0.000      1.293      0.196      -0.000       0.002\n",
      "n_estimators_300               0.0022      0.000      4.595      0.000       0.001       0.003\n",
      "subsample_1.0                  0.0006      0.000      1.336      0.182      -0.000       0.002\n",
      "==============================================================================\n",
      "Omnibus:                      139.191   Durbin-Watson:                   0.917\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              381.431\n",
      "Skew:                          -1.186   Prob(JB):                     1.49e-83\n",
      "Kurtosis:                       6.205   Cond. No.                         7.90\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC3)\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=True)\n",
    "\n",
    "model = OLS(endog=data.test_score, exog=add_constant(data.drop('test_score', axis=1))).fit(cov_type='HC3')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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